Integrated power generation equipment health state supervision system

Abstract

The invention provides an integrated power generation equipment health state supervision system. Comprising the following steps: acquiring operation parameters and environment parameter data of an asynchronous motor precision point inspection diagnosis device, a TLI-X8 battery online monitoring system, a collecting ring intelligent data acquisition system, a transformer oil dissolved gas and micro water online monitoring system, a generator partial discharge online monitoring device and a wireless temperature measurement system; and training prediction is performed to obtain fault prediction data, so that accurate fault diagnosis can be realized on the basis of extracting original data features by deep learning. According to the application, an asynchronous motor precision point inspection diagnosis device, a transformer oil gas analysis system, a partial discharge detection system, a wireless temperature measurement system, a battery online monitoring system and the like are organically combined, and power generation equipment faults are predicted through the mother convolutional neural network model and the sub-machine learning model, so that early fault discrimination is realized; and the accuracy and efficiency of equipment fault identification are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor equipment control, and particularly to an integrated power generation equipment health status supervision system. Background Art

[0002] The power industry generally faces prominent problems such as high operation and maintenance costs, difficulty in predicting equipment failures, and fragmentation of system data in ensuring the safe operation of power generation equipment and stable power supply. In order to monitor the operation status of key components, many power generation enterprises have successively deployed multiple independent online monitoring or diagnosis systems, such as battery online monitoring systems, slip ring intelligent data acquisition systems, on-line monitoring systems for dissolved gases and trace water in transformer oil, generator partial discharge on-line monitoring devices, and wireless temperature measurement systems. These systems can collect data and perform partial analysis on the equipment within their respective monitoring scopes. However, due to the lack of a unified integration platform, information islands are often formed. On the one hand, operation and maintenance personnel need to face several sets of software or terminals simultaneously, and panoramic management cannot be achieved on a single platform. On the other hand, cross-system data correlation analysis and comprehensive evaluation of equipment health status are difficult to carry out, resulting in potential fault hazards that cannot be identified in a timely manner. In addition, as the operating conditions of power plants become increasingly complex, traditional "periodic maintenance" or "planned maintenance" modes often encounter problems such as "undetected maintenance" or "over-inspection" when facing rapidly changing loads and frequent peak shaving operations. Undetected maintenance will bring serious safety hazards and cause unplanned outages, while over-inspection leads to repeated disassembly and inspection of equipment, wasting human and spare part resources.

[0003] At the same time, core auxiliary equipment such as asynchronous motors plays an important role in power plants. However, under high load and frequent start-stop conditions, they are extremely prone to progressive faults such as stator winding turn-to-turn faults, rotor bar breakage faults, and bearing faults. If these faults cannot be detected in the early stage, they are very likely to evolve into major accidents, causing large-scale outages and huge economic losses. Although there are existing local monitoring means to collect signals such as voltage, current, and vibration of motors, the monitoring strategy based on a single index is difficult to accurately and comprehensively reflect the fault occurrence mechanism, and it is also difficult to provide refined prediction and warning for maintenance personnel in a timely manner.

[0004] On the other hand, with the rapid development of information technology and Internet of Things technology, how to uniformly connect various detection devices in power plants to a comprehensive platform and conduct integrated analysis of massive historical data and real-time data has become a common concern in the industry. Only by realizing the interconnection and interoperability of production monitoring systems can multi-source heterogeneous data be deeply mined to make rapid and reliable early warning decisions after detecting signs of faults. In recent years, some power plants have also tried to establish equipment health management platforms to integrate and visualize unit operation parameters and monitoring alarm information. However, in the specific implementation process, due to large system differences and inconsistent data standards and interface protocols, the integration cost is high and the expansion ability is weak, making it difficult to meet the access requirements of subsequent new monitoring devices. In addition, the traditional fault judgment method based on static thresholds or fixed models cannot fully cope with the dynamic changes of motor loads and environmental conditions, thus restricting the in-depth development of equipment intelligent diagnosis and precise operation and maintenance. Moreover, in the existing technology, all power generation equipment is regarded as a system and a single prediction model is used for fault judgment, without a hybrid recognition model for the overall system and sub-critical systems, resulting in a low accuracy rate of fault judgment; and the existing prediction model only conducts training and prediction on data, without considering equipment parameter factors in network activation functions, etc., and without the addition of a physical constraint layer, resulting in a significant reduction in prediction accuracy, and without an identification method with key intermediate data as input, making the prediction efficiency and accuracy of equipment health status relatively low.

[0005] To sum up, how to build a comprehensive supervision platform that can not only integrate the monitoring devices already deployed in power plants but also conduct high-precision fault identification and real-time health assessment of key equipment such as motors has become a key requirement for ensuring the safe and stable operation of power generation equipment. By accessing multi-source monitoring systems through a unified hardware interface and software platform and supplementing with advanced algorithms to achieve multi-dimensional fault diagnosis and situation awareness, it is expected to significantly reduce the operation and maintenance costs and improve the equipment health management level, which is in line with the development direction of smart power plants and digital transformation. Therefore, there is an urgent need for an integrated power generation equipment health status supervision system that can organically combine systems such as asynchronous motor precision inspection and diagnosis devices, gas analysis in transformer oil, partial discharge detection, wireless temperature measurement, and battery online monitoring, and on this basis provide a visual human-machine interaction interface and an intelligent early warning mechanism to achieve early fault identification and lean management of the entire life cycle of equipment. Summary of the Invention

[0006] In view of the above problems mentioned in the prior art, the present invention provides an integrated power generation equipment health status supervision system, including a multi-source data access module, a wireless communication module, a data storage module, a fault diagnosis and evaluation module, a warning module, and a visual human-computer interaction module. By collecting the operation parameters and environmental parameter data of an asynchronous motor precision inspection and diagnosis device, a TLI-X8 battery online monitoring system, a slip ring intelligent data acquisition system, a transformer oil dissolved gas and micro-water online monitoring system, a generator partial discharge online monitoring device, and a wireless temperature measurement system, fault prediction data is obtained through training and prediction. This application organically combines systems such as an asynchronous motor precision inspection and diagnosis device, transformer oil gas analysis, partial discharge detection, wireless temperature measurement, and battery online monitoring, and uses a mother convolutional neural network model and a sub-machine learning model to predict power generation equipment failures, so as to achieve early fault identification, greatly improving the accuracy and efficiency of equipment fault identification.

[0007] This application provides an integrated power generation equipment health status supervision system, including: A multi-source data access module, used to access an asynchronous motor precision inspection and diagnosis device, a TLI-X8 battery online monitoring system, a slip ring intelligent data acquisition system, a transformer oil dissolved gas and micro-water online monitoring system, a generator partial discharge online monitoring device, and a wireless temperature measurement system, and obtain the operation parameters and environmental parameter data of the power generation equipment; A wireless communication module, which transmits the collected operation parameters and environmental parameter data to the data storage module; A data storage module, used to store the operation parameters and environmental parameter data from the multi-source data access module; A fault diagnosis and evaluation module, used to perform global training calculation on the obtained operation parameters and environmental parameter data of the power generation equipment using a mother convolutional neural network model to output a health evaluation level, and use a sub-machine learning model to calculate and judge a specific fault mode to output a fault risk level; A warning module, used to generate an alarm signal when the health evaluation level or the fault risk level is respectively lower than the corresponding set threshold; A visual human-computer interaction module, used to display the operation status, historical records, alarm information, and health evaluation level of the power generation equipment on the platform interface or mobile terminal.

[0008] Preferably, the asynchronous motor precision inspection and diagnosis device includes: An on-site diagnosis unit, installed inside the motor control cabinet, used to collect the three-phase voltage and current signals of the motor and perform real-time fault feature extraction; An asynchronous motor precision inspection communication module, used to transmit the monitoring data and processing results of the on-site diagnosis unit to the data storage module; The asynchronous motor precision on-site inspection local alarm unit is used for on-site audible and visual alarm when motor fault signs are detected; The on-site diagnosis unit obtains the three-phase voltage and current of the motor through the secondary side signals of the voltage transformer and the current transformer. Under different load and power supply voltage fluctuation conditions, the inter-turn fault of the motor stator winding, the rotor broken bar fault and the bearing fault are identified by the negative sequence apparent impedance filtering value and the stator current modulus spectrum method.

[0009] Preferably, the obtaining of the operation parameters and environment parameter data of the power generation equipment includes: The environment parameters include obtaining the temperature information collected by the temperature sensor. The temperature sensor is attached to the high-voltage switch contact or the bus joint, and the sampling frequency is not less than 1 kHz; The operation parameters include obtaining the mechanical vibration and micro-crack acoustic wave signals collected by the vibration sensor. The vibration sensor integrates a MEMS gyroscope and an acoustic emission probe and is used to monitor the motor bearing state; The operation parameters also include the three-phase current waveform data collected by the current transformer and the three-phase voltage waveform data collected by the voltage transformer.

[0010] Preferably, the data storage module includes: A real-time database for storing the monitoring data of the most recent time or continuously in a short period of time; A historical database for storing long-term monitoring data in a time series to support trend analysis, fault tracing and health modeling; A data interface management unit for receiving the raw data from the multi-source data access module and writing it into the real-time database after standardization processing.

[0011] Preferably, the multi-source data access module realizes data integration in the following ways: Time series alignment is performed on the battery monitoring data collected by the TLI-X8 battery on-line monitoring system; Spectral analysis technology is used for the dissolved gas data in transformer oil collected by the on-line monitoring system for dissolved gas and micro-water in transformer oil; High-frequency signal filtering and feature extraction are used for the partial discharge data collected by the on-line monitoring device for partial discharge of the generator.

[0012] Preferably, the fault diagnosis and evaluation module includes: Adopt a mother convolutional neural network model to perform feature extraction calculation on the input vector composed of historical and real-time input voltage, current, negative sequence impedance, harmonics, temperature and motor load conditions, and output a health evaluation level to improve the sensitivity and robustness of fault diagnosis; The data input into the mother convolutional neural network model also includes the feature data obtained by calculating the fault diagnosis algorithm library.

[0013] Preferably, the fault diagnosis algorithm library is based on the following method: For the inter-turn fault of the stator winding, the negative sequence current component analysis is used to generate the first analysis data; For the rotor bar breakage fault, the detection of the frequency component of (1±2s)*f in the stator current spectrum is used to generate the detection data; where s represents the slip ratio of the asynchronous motor, and f is the fundamental frequency of the power supply; For the bearing fault, the joint time-frequency domain analysis of the vibration signal is used to generate the second analysis data.

[0014] Preferably, the warning levels of the warning module include: three levels of real-time alarm, early warning, and prompt information, corresponding to different processing strategies for emergency situations, suspected faults, and normal states respectively.

[0015] Preferably, the visual human-machine interaction module further includes a digital twin module, which displays the operation state of the device in real time through 3D modeling and simulates the fault evolution process.

[0016] Preferably, the digital twin module supports the following functions: Dynamic visualization of device configuration; Superposition display of infrared thermal imaging and real-time data; Matching of historical fault case library and recommendation of maintenance strategies.

[0017] Preferably, it further includes a data interface of the SCADA system to achieve seamless integration of the partial discharge intensity PDI signal and temperature data.

[0018] Preferably, the precision inspection and diagnosis of the asynchronous motor includes the following steps: Real-time collection of the stator current, voltage, and vibration signals of the motor; Extraction of negative sequence impedance characteristics and spectral characteristics; Judging the fault risk level of specific fault modes through a sub-machine learning model; specific fault modes include inter-turn short circuit, rotor bar breakage, and bearing fault; Generating maintenance suggestions and pushing them to the user terminal.

[0019] Preferably, the sub-machine learning model adopts a hybrid architecture of a deep neural network and a support vector machine; the mother convolutional neural network model and the sub-machine learning model work in parallel or sequentially on the background server or the same hardware platform respectively, and their data streams and operation processes are independent and there is no conflict.

[0020] Preferably, the maintenance suggestions displayed by the visual human-machine interaction module also include: The fault location accuracy is not less than 95%; Recommendation of maintenance time window; Spare parts inventory matching information.

[0021] Preferably, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture, including: A physical constraint neural network PC-DNN, which embeds the motor electromagnetic-mechanical coupling equation as a physical constraint layer in the deep neural network, and is implemented in the following ways: In the stator winding fault diagnosis branch, the Maxwell's equations are discretized into differentiable operators to force the network output to satisfy the electromagnetic field distribution law; In the bearing fault diagnosis branch, the stress distribution calculated by the Hertz contact theory is injected into the initialization of the convolutional kernel weights as prior knowledge.

[0022] Preferably, the sub-machine learning model further includes: A dynamic graph attention support vector machine DGAT-SVM, which constructs a dynamic graph structure based on the device operation topology relationship, where: The node features include the real-time current harmonic distortion rate, vibration spectrum entropy value, and temperature gradient; The edge weights are dynamically updated according to the electrical connection strength between devices and the historical fault propagation probability; The attention mechanism is used to focus on the key fault correlation paths, and the device-level and system-level risk scores are output.

[0023] Preferably, the sub-machine learning model further includes: A cross-domain incremental learning module, including: A simulation-measured data alignment unit, which uses the adversarial domain adaptation ADA technology to eliminate the feature distribution deviation between the simulation model and the real device; An online knowledge distillation pipeline, which encodes the expert diagnosis rules in the historical fault case library into a lightweight decision tree and outputs for consistency constraint.

[0024] Preferably, the sub-machine learning model further includes: An uncertainty quantification engine, which evaluates the credibility of the diagnosis results in the following ways: Apply Monte Carlo Dropout perturbation to the output of the PC-DNN to calculate the confidence interval of the fault probability distribution; Introduce a fuzzy membership function in the DGAT-SVM to quantify the progressive process of the device state deviating from the normal threshold.

[0025] Preferably, the training data of the sub-machine learning model further includes: A multi-physical field coupling fault data set based on finite element simulation, covering rotor eccentricity and insulation aging composite fault scenarios; The carbon footprint data of the device throughout its life cycle, which is used to correlate the fault mode and the energy efficiency degradation relationship.

[0026] Preferably, the wireless communication module includes a heterogeneous communication gateway, supports dual-mode communication of LoRa low-power wide-area network and 5G network, and adopts a dynamic routing algorithm based on the device topology structure.

[0027] Preferably, the activation function adopted by the mother convolutional neural network model is expressed as follows: ; The activation function adopted by the physical constraint neural network of the sub-machine learning model is expressed as follows: ; wherein, represents the input value of the current neuron, e is the base of the natural logarithm; s represents the slip ratio of the asynchronous motor, f is the fundamental frequency of the power supply, represents the electrical side frequency offset monitored in real time, that is, the difference from the nominal frequency of 50 Hz; is the dynamic change amount on the environment side, that is, the sum of the normalized temperature, wind speed, and load changes; is the coupling coefficient, and the larger the value, the more sensitive it is to the changes in the dynamic characteristics of the motor.

[0028] The present invention provides an integrated power generation equipment health status supervision system, and the beneficial technical effects that can be achieved are as follows: 1. The present invention organically integrates the battery online monitoring, slip ring intelligent data acquisition, dissolved gas analysis in transformer oil, partial discharge monitoring, wireless temperature measurement, and asynchronous motor precision inspection and diagnosis devices that are traditionally deployed separately, and realizes the centralized acquisition and visualization management of multi-source heterogeneous data through a unified interface protocol and data management module, greatly reducing the workload of maintenance personnel repeatedly switching between different systems, and also avoiding the problem of information islands, laying a good foundation for subsequent system expansion and integration. By appending the characteristic data (such as negative sequence current component, spectrum characteristics, etc.) calculated by the fault diagnosis algorithm library to the input layer of the mother convolutional neural network model, the present invention can directly fuse the "prior knowledge" of various classic diagnosis methods on the basis of deep learning to extract the characteristics of the original data. This not only further enriches the input dimension of the model, but also ensures the complementary advantages of the deep network and traditional diagnosis characteristics, double improving the reliability and sensitivity of fault detection, and enabling more accurate and timely identification of early fault symptoms.

[0029] 2. Through the fault diagnosis and evaluation module, relying on advanced algorithms such as the fusion of the mother convolutional neural network and the physical constraint sub-network, the present invention can quickly identify the early signs of inter-turn faults in the motor stator winding, rotor broken bar faults, bearing faults, etc. under complex working conditions, and significantly improve the sensitivity and accuracy of fault identification by combining technologies such as negative sequence apparent impedance, stator current modulus spectrum, and multi-modal meta-learning. Once the health assessment level is monitored to be lower than the threshold, the system can automatically trigger an early warning and push maintenance suggestions to avoid unplanned shutdowns caused by the further deterioration of faults. The present invention predicts the faults of power generation equipment through the mother convolutional neural network model and the sub-machine learning model to achieve early fault identification, greatly improving the accuracy and efficiency of equipment fault identification. The present invention reasonably introduces physical or working condition information such as motor slip ratio and environmental interference coefficient into the activation function of the mother convolutional neural network model, so that the input of the activation function is no longer just the result of a simple linear transformation, and the network can adaptively adjust the output sensitivity when facing changing operating conditions. This improved activation function can not only enhance the robustness to complex factors such as electrical side imbalance and load fluctuation, but also effectively prevent gradient disappearance or gradient explosion, thus shortening the convergence time while maintaining high-precision diagnosis.

[0030] 3. By adding a physical constraint neural network (PC-DNN) to the sub-machine learning model and embedding the motor electro-magnetic-mechanical coupling equation into the deep network, the present invention enables the network to not only rely on data-driven during the training process, but also impose constraints on the network output through physical equations to force it to satisfy the internal physical laws of the motor. This can significantly reduce the dependence on ultra-large-scale labeled data, improve the prediction accuracy and stability in complex working conditions or scenarios with insufficient samples, and effectively avoid the network output from producing results that are contrary to the true physical mechanism, greatly enhancing the reliability and interpretability of fault diagnosis. Introducing a dynamic graph attention support vector machine into the multi-modal meta-learning architecture can, on the one hand, construct a dynamic graph structure model based on the equipment operation topology relationship and dynamically update the weights of nodes and edges; on the other hand, the attention mechanism can focus on the key fault correlation paths to achieve accurate identification of the global fault propagation link. This combination greatly improves the ability to evaluate the system-level fault risk, can quickly extract key features in the context of complex fault correlations between multiple devices, reduce the interference of irrelevant information, and thus significantly enhance the fault prediction efficiency and real-time response ability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0032] Figure 1 Schematic diagram of an integrated power generation equipment health status supervision system of the present invention; Figure 2 Schematic diagram of an on-line diagnosis and early warning workstation for initial faults of an electric motor of the present invention; Figure 3 Schematic diagram of an electric motor status monitoring and initial fault early warning system of the present invention. Specific implementation manners

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0034] Embodiment 1: In view of the above problems mentioned in the prior art, to solve the above technical problems, as shown in the attached Figure 1 figure, this embodiment provides an integrated power generation equipment health status supervision system, which may include an asynchronous motor precision inspection and diagnosis device, a TLI-X8 battery on-line monitoring system, a slip ring intelligent data acquisition system, a wireless temperature measurement system, an on-line partial discharge monitoring device for a generator, an on-line monitoring system for dissolved gases and trace water in transformer oil, a calculation unit, an alarm unit, and a platform interface or a customer terminal (such as a PC, a mobile phone APP).

[0035] Among them, the above-mentioned precision on-site inspection and diagnosis device for asynchronous motors, TLI-X8 battery online monitoring system, slip ring intelligent data acquisition system, wireless temperature measurement system, on-line partial discharge monitoring device for generators, and on-line monitoring system for dissolved gases and trace water in transformer oil obtain original data such as electricity, temperature, vibration, and gas concentration in real time through dissolved gas sensors in transformer oil, and send them out via wired or short-range wireless (RS485, CAN, LAN, etc.). The computing unit undertakes data aggregation, storage, operation, and communication functions, and is internally divided into four core modules: a) LoRa / 5G: responsible for low-power wide-area / high-speed data backhaul of remote or dispersed devices; b) data storage: including real-time database and historical database, used to cache high-frequency sampling values and long-term trend data; c) CPU / GPU: deploying parent convolutional neural network and sub-machine learning models to achieve health score, fault identification, and maintenance window prediction; d) field switch / gateway: docking with the industrial Ethernet in the factory area, uniformly transferring data from each monitoring device and LoRa / 5G link into the computing unit, and at the same time distributing model parameters or control instructions downward. The above modules are interconnected through high-speed buses or gigabit Ethernet to ensure millisecond-level data transfer. All monitoring data first enters the data storage, and then the CPU / GPU completes parallel or sequential reasoning; the risk values and health indexes output by the model are written back to the database for subsequent calls. The alarm unit is set independently. When the risk level given by the computing unit reaches the set threshold, it immediately triggers local audible and visual alarms, SMS / APP push, or relay protection actions to ensure timely and effective accident prevention. The platform interface or customer terminal can be, for example, a PC or a mobile phone APP, which calls the computing unit database through a standard API to visually display real-time curves, digital twin three-dimensional models, alarm records, and maintenance suggestions; at the same time, it allows operation and maintenance personnel to remotely confirm alarms, issue inspection work orders, and view spare parts inventory matching information.

[0036] Specifically, the precision on-site inspection and diagnosis device for asynchronous motors can include an on-site diagnosis unit, which can be installed inside the motor control cabinet and includes a high-speed sampling board, a signal conditioning module, and a local processing CPU, and can implement real-time fault feature extraction of voltage and current; an alarm unit: when serious fault symptoms occur in the motor, it gives audible and visual warnings on-site; the communication module in the computing unit uploads the collected and diagnosed data to the gateway in the form of RS485, industrial Ethernet, or using LoRa / 5G, etc. This communication module can include Figure 1The LoRa / 5G and TLI-X8 battery online monitoring device therein has a built-in sensor module that can monitor parameters such as battery voltage, internal resistance, and temperature. The generator partial discharge online monitoring device (such as W-PD6) can include a high-frequency sensor and a front-end acquisition unit for amplifying and filtering partial discharge pulse signals. The wireless temperature measurement system (such as XK-WCX) can include wireless temperature sensors that are attached to high-voltage contacts or other heat sources and report temperature measurement data regularly. The online monitoring system for dissolved gases and trace water in transformer oil (such as TRANSFIX™) includes a photoacoustic spectroscopy measurement unit that can separate and detect the gas concentration and trace water content in the oil through the photoacoustic spectroscopy measurement unit. The collector ring intelligent data acquisition system can collect carbon brush current radar charts, temperature, vibration, and other status information through the ring-side device. The platform interface or customer terminal (such as a PC or mobile APP) can perform alarm push. The operation and maintenance personnel can receive the alarm push through the platform interface or customer terminal (such as a PC or mobile APP), view the details of fault diagnosis, and record on-site inspections.

[0037] In some embodiments, the above system can further include: The gateway / interface module packs the monitoring data and uploads it through a LAN or wireless network. Among them, the interface module is connected to the upper system and usually supports industrial protocols such as Modbus TCP / OPC UA. The communication gateway uploads the obtained discharge intensity (PDI) or pulse characteristics to the calculation unit. The centralized receiving gateway interacts with the calculation unit through industrial Ethernet or wireless means. The on-site switch / hub aggregates the network ports of monitoring devices such as the asynchronous motor precision inspection and diagnosis device, the TLI-X8 battery online monitoring system, and the collector ring intelligent data acquisition system to the factory LAN or a dedicated industrial Ethernet. The wireless communication module or Internet of Things gateway is deployed in scenarios such as LoRa / 5G that require large-scale, low-power, or high-speed transmission to ensure stable access of each device.

[0038] The data memory deploys a real-time database and a historical database for temporary caching and long-term archiving of high-frequency data. The CPU / GPU is used to run intelligent algorithms such as the master convolutional neural network model and the sub-machine learning model. The platform interface provides a visual human-computer interaction interface, a digital twin module, as well as alarm management and reporting functions. The customer terminal facilitates the duty personnel to view the status of multiple devices on the centralized monitoring screen.

[0039] In some embodiments, the above system can further include: a multi-source data access module that is used to access the asynchronous motor precision inspection and diagnosis device, the TLI-X8 battery online monitoring system, the collector ring intelligent data acquisition system, the online monitoring system for dissolved gases and trace water in transformer oil, the generator partial discharge online monitoring device, and the wireless temperature measurement system to obtain the operation parameters and environmental parameter data of the power generation equipment.

[0040] In some embodiments, the above system may further include: an on-line monitoring device for generator insulation overheating and a monitoring module for hydrogen dryer, which are used to monitor the overheating of generator insulation and the humidity of the output gas of the hydrogen dryer device.

[0041] Among them, information such as temperature, humidity, dew point, alarm flag, etc. collected by the on-line monitoring device for generator insulation overheating and the monitoring module for hydrogen dryer are accessed to the multi-source data access module through a unified protocol or gateway, and are summarized to the calculation unit together with the collected data of devices such as the TLI-X8 battery on-line monitoring device, the on-line monitoring system for dissolved gases and micro water in transformer oil, the on-line monitoring device for partial discharge of generator, and the wireless temperature measurement system. The on-line monitoring device for generator insulation overheating is incorporated into the generator hydrogen cooling system through a dedicated pipeline and operates. The operating conditions of the generator are judged by monitoring whether the hydrogen contains insulation overheating decomposition substances. The device is connected to the inside of the generator through the "inlet" and "outlet" to form a closed circulation system. Under the action of the generator fan pressure, the cooling gas enters the "ion chamber" in the device. After being bombarded by the α rays released by the radiation source Am241 in the ion chamber, the cooling gas medium is ionized to generate positive and negative ion pairs (hydrogen ion pairs for hydrogen-cooled units). At this time, a DC electric field is applied to the ion chamber, and the electric field makes the positive and negative ion pairs move in a directional manner to form an extremely weak ionization current (10-12A). This current is amplified (1010 times) by an amplifier and then displayed on the liquid crystal screen.

[0042] In some embodiments, the on-line monitoring device for generator insulation overheating not only realizes the identification of the insulation state by monitoring whether there are insulation decomposition products (such as acetylene, methane, carbon dioxide, etc.) in the hydrogen cooling system, but also combines parameters such as the concentration trend of decomposition products, hydrogen humidity, temperature rise rate and operating load to comprehensively judge the operating conditions of the generator insulation system. The specific implementation method is as follows: First, a high-sensitivity gas sensing module is provided in the device. Using the infrared absorption spectroscopy method or a gas sensor array, the change in the concentration of trace gases in the hydrogen flow is periodically collected. After the sensing signal is processed by a current amplification circuit (with an amplification factor such as 1010 times), it is converted into a digital signal and transmitted to the monitoring main control unit. The main control unit is built-in with a multi-parameter analysis algorithm to fit the growth rate of the decomposition gas concentration within a specific time window and extract the characteristic curve of the insulation thermal decomposition trend. Secondly, the system cross-compares the above data with the current operating load of the generator, the temperature of the cooling gas, and the temperature rise value at the machine terminal. When it is detected that the concentration of a certain type of decomposition gas (such as CO2 or C2H2) continues to increase, and at the same time the hydrogen humidity increases, the temperature rise rate is abnormal, or the unit operates in a high-load range for a long time, the system can determine that the current generator is in a state of mild thermal aging or overheating warning of the insulation; if the gas concentration jumps, the temperature rise slope increases sharply and is accompanied by other abnormal signals (such as an increase in the partial discharge intensity), it is determined as a risk of insulation overheating or a precursor to breakdown. In addition, to avoid misjudgment, the system is built-in with a dynamic alarm threshold adjustment mechanism to dynamically correct the alarm limit according to the operating conditions (such as start-stop frequency, cooling air volume, ambient temperature) to ensure accurate response in different power plant site environments. The monitoring results are displayed on the liquid crystal screen in terms of the insulation risk level (normal, suspicious, serious) and are synchronously uploaded to the SCADA system for remote analysis and alarm linkage.

[0043] The online monitoring device for overheating of generator insulation is mainly used for real-time monitoring of the risks of high temperature, hot spots, degradation, etc. that occur in the insulation layer of the generator stator winding or rotor coil during operation. The device is usually composed of a high-precision temperature sensor, a temperature transmitter module, a data processing unit, and a communication gateway. It can also be combined with an online thermal analysis algorithm to provide early warning of overheating of the insulation material or abnormal local temperature rise. Key monitoring parameters Winding surface temperature: Built-in or applied temperature sensors are arranged near the winding insulation layer or rotor slots to continuously obtain the temperature change value of the high-temperature zone inside the motor; Temperature gradient: By comparing the temperature difference or temperature rise rate of multiple points of the winding, possible hot spots or insulation degradation trends can be identified; Thermal stress assessment: In conjunction with the built-in algorithm of the device, based on the historical temperature-time curve and operating load conditions, the thermal stress accumulation of the insulation material is quantified, thereby evaluating the insulation life and safety margin. Device functions and data interface, local data processing: some monitoring devices can complete temperature curve analysis locally and generate overheating warning indicators; communication method: usually RS485 / Modbus, Ethernet or optical fiber network is used to connect to the host system, and the temperature measurement value and gradient alarm information are sent to the multi-source data access module; integration with other systems: the insulation overheating alarm signal can be correlated with the discharge intensity (PDI) data of the generator partial discharge online monitoring device to form a comprehensive judgment on the generator insulation fault. Prevention of insulation aging failure: real-time grasp of the actual temperature changes of the winding or rotor coil is helpful to early detection of thermal degradation of the insulation layer or local overheating points; improve safety and economy: if the hot spot is abnormally heated, the system can give an alarm in time to remind the operator to take measures, such as maintenance or other protective measures, thereby reducing the loss of unexpected downtime.

[0044] The hydrogen dryer device is mainly used for the purification and drying treatment of hydrogen in large steam turbines or hydrogen-cooled generator sets, maintaining high-purity and low moisture content of hydrogen to reduce the adverse effects of water vapor inside the generator on the insulation of the stator and rotor. The device includes core components such as a hydrogen drying tank, adsorption material, temperature and humidity sensor, and automatic control valve, and realizes continuous dehumidification of hydrogen through the drying cycle or regeneration cycle mode. Key monitoring parameters: Hydrogen humidity / dew point: The moisture content or dew point value in the hydrogen flow is detected by an internal or external humidity transmitter to judge the drying effect; Hydrogen purity: Some hydrogen dryer devices are equipped with a gas analyzer to measure the impurity or oxygen content in hydrogen to ensure the safe operation of the unit; Dryer operating status: Such as adsorbent saturation, regeneration process temperature, gas flow, etc., are used to determine when to switch between the drying cycle and the regeneration cycle. Device functions and data interfaces, Automatic control: Through the PLC or embedded control unit, the hydrogen dryer device can automatically switch the working state according to the feedback of the humidity sensor and hydrogen flow, ensuring that hydrogen remains dry for a long time; Data upload: Regularly or in real-time send indicators such as humidity value, dew point, pressure, purity, etc. to the multi-source data access module, and the communication method can use industrial Ethernet, RS485 / Modbus or a dedicated protocol; Alarm output: If the humidity exceeds the limit or the adsorbent is saturated and cannot be regenerated, the device will issue an alarm and push it to the upper-level system to remind the operation and maintenance personnel to replace or repair in time. Suppress insulation dampness: Effectively reduce the moisture content of the hydrogen cooling system and reduce the risk of failures caused by insulation dampness; Improve the cooling efficiency of the generator: Ensuring high-purity hydrogen helps to strengthen heat conduction and reduce heat generation, thus maintaining the generator operating in the high-efficiency range; Extend the equipment life: Cooperating with the on-line monitoring device for generator insulation overheating, the system can more comprehensively master the cooling state and insulation health, making it easier to detect and handle potential faults earlier.

[0045] In some embodiments, the integrated power generation equipment health status supervision system may further include: sensors / transformers. The sensors may include temperature sensors, and vibration sensors / acoustic emission probes; The temperature sensors are attached to the high-voltage switch contacts or bus joints to monitor the temperature and provide the equipment heating situation to the upper-level system; The vibration sensors / acoustic emission probes are installed on the motor bearings or other key parts to collect mechanical vibration and acoustic wave signals; The transformers may include current transformers and voltage transformers. The current transformer (CT) outputs a 1A signal through the secondary side to achieve safe acquisition of the three-phase current waveform; The voltage transformer (PT) transforms the high voltage into a low voltage signal that can be processed, facilitating accurate measurement of the motor or bus voltage waveform.

[0046] For the access of sensors / transformers and the in-situ diagnosis unit in the precision on-site inspection and diagnosis device of asynchronous motors, signals such as the secondary sides of PT and CT and vibration / acoustic emission probes can be connected to the in-situ diagnosis unit by cable; after being collected by the in-situ diagnosis unit, preliminary calculations (such as negative sequence impedance or spectral characteristics) are performed and cached internally if necessary. For the access of the in-situ diagnosis unit to the on-site switch / gateway, if the in-situ diagnosis unit has an industrial Ethernet port, it can be directly connected to the on-site switch and then transmitted to the computing unit through the in-plant LAN; if the on-site environment is more suitable for RS485 / Modbus RTU or wireless communication, it can be connected to the computing unit through the on-site switch or gateway. For the access of each special monitoring device (such as the TLI-X8 battery online monitoring system, the on-line monitoring system for dissolved gases and trace water in transformer oil, the on-line monitoring device for partial discharge in generators, etc.) to the computing unit, monitoring data is usually uploaded using Ethernet (TCP / IP), RS485 / Modbus, CAN bus or wireless solutions; and is uniformly parsed and standardized by the multi-source data access module and then written into the database in the data storage. Inside the computing unit, the data storage is interconnected with the CPU / GPU through a high-speed local area network or a storage network; the CPU / GPU reads the monitoring data from the database in real time or directly subscribes to the sensor data stream and runs the master CNN and sub-machine learning models. The processing / analysis results are shared with the platform interface or customer terminals (such as PCs, mobile phone APPs) through WebSocket, REST API or industrial protocol interfaces. For the access of the alarm unit to the platform interface or customer terminals (such as PCs, mobile phone APPs), the alarm unit pushes the monitoring curves and alarm information to the large screen in the centralized control room or the SCADA workstation; at the same time, important alarms are pushed to the platform interface or customer terminals (such as PCs, mobile phone APPs) through APPs or communication software, etc., supporting remote viewing and confirmation.

[0047] Embodiment 2, the present invention also provides an integrated power generation equipment health status supervision system, which may include: a multi-source data access module, a wireless communication module, a data storage module, a fault diagnosis and evaluation module, a warning module, and a visual human-computer interaction module. Among them, the multi-source data access module is used to access the precision on-site inspection and diagnosis device of asynchronous motors, the TLI-X8 battery online monitoring system, the slip ring intelligent data acquisition system, the on-line monitoring system for dissolved gases and trace water in transformer oil, the on-line monitoring device for partial discharge in generators, and the wireless temperature measurement system, and obtain the operation parameters and environmental parameter data of the power generation equipment; The wireless communication module transmits the collected operation parameters and environmental parameter data to the data storage module; The data storage module is used to store the operation parameters and environmental parameter data from the multi-source data access module; A fault diagnosis and evaluation module is used to perform global training calculations on the obtained operation parameters and environmental parameter data of power generation equipment using a mother convolutional neural network model to output a health evaluation level, and use a sub-machine learning model to calculate and judge specific fault modes to output a fault risk level; An early warning module is used to generate an alarm signal when the health evaluation level or the fault risk level is lower than the corresponding set threshold; A visual human-computer interaction module is used to display the operation status, historical records, alarm information, and health evaluation level of power generation equipment on the platform interface or mobile terminal.

[0048] Optionally, the multi-source data access module is deployed between the integrated monitoring room and the main control building of the power plant, and realizes the data acquisition and management of various online monitoring devices through a wired and wireless parallel communication network. Specifically: Access to the precision on-site inspection and diagnosis device for asynchronous motors. The local diagnosis unit in each important asynchronous motor control cabinet is connected to the multi-source data access module through industrial Ethernet or RS485 bus. The three-phase voltage, current, and fault characteristic values obtained by online diagnostic operations such as negative sequence impedance algorithm and spectrum analysis collected by this local diagnosis unit will be automatically timestamped and uploaded to the multi-source data access module.

[0049] Access to the TLI-X8 battery online monitoring system. For the UPS battery bank, the TLI-X8 system outputs real-time battery voltage, current, temperature, internal resistance and other parameters through its own gateway. The multi-source data access module establishes a connection with it according to the Modbus TCP or OPC UA protocol supported by this gateway, periodically reads and stores key performance indicators. When there is a deterioration trend of a single battery, the system can issue an early warning in real time.

[0050] Access to the slip ring intelligent data acquisition system. The carbon brush current radar chart, temperature, vibration and other status information are collected by the ring-side device and transmitted to the multi-source data access module through the TCP / IP protocol. To cope with high-frequency data, a high-speed buffer is set inside the module for data compression and preprocessing to reduce the pressure on the background server.

[0051] Access to the on-line monitoring system for dissolved gases and micro water in transformer oil. The TRANSFIX™ device obtains the transformer fault gas content and oil moisture data through a dedicated photoacoustic spectroscopy measurement unit on site, and then transmits the measurement results to the multi-source data access module in serial or Ethernet mode. The module performs standardized conversion on the received gas concentration data and combines it with the oil service management regulations used by the main control system to judge the health of the transformer insulation state.

[0052] Access of on-line monitoring device for partial discharge of generator. The W-PD6 device is responsible for collecting the partial discharge intensity (PDI) and high-frequency discharge waveform of the generator. Its signals are transmitted to the data acquisition end after high-frequency isolation and then transmitted to the multi-source data access module in parallel through fiber optic Ethernet or CAN interface. The module will compare the real-time value of partial discharge with the reference threshold. When an anomaly occurs, it will trigger an alarm and push it to the background server.

[0053] Access of wireless temperature measurement system. The XK-WCX type temperature measurement device attaches the sensor to the high-voltage switch contact or bus joint and sends temperature data to the centralized gateway through wireless radio frequency; then the gateway is integrated with the multi-source data access module through an Ethernet connection. The multi-source data access module correlates and analyzes the real-time temperature measurement value with parameters such as ambient temperature and load current to identify the potential possibility of overheating or loose connection faults.

[0054] Through the above methods, the multi-source data access module can collect electrical parameters (such as voltage, current, gas content in transformer oil, etc.) and environmental parameters (such as temperature, vibration, etc.) at the same time, and package and upload the monitoring data provided by different devices to the data storage module for unified management. This not only ensures the normal independent operation of each monitoring system, but also realizes cross-platform data integration and centralized monitoring. In case of an abnormal situation, the system can push information to the fault diagnosis and evaluation module and the visualization interface in the first time, greatly improving the efficiency of identifying and handling potential faults of power generation equipment.

[0055] The wireless communication module uses Internet of Things (IoT) technology to achieve remote data transmission. The wireless communication module complements the wired network inside the power plant and uses IoT technology to achieve remote and secure transmission and centralized management of the data of power generation equipment. In some embodiments, its working process is generally as follows: The wireless communication module is equipped with a heterogeneous communication gateway to support dual-mode communication of the mainstream low-power wide area network (LoRa) and high-speed 5G network. For the relatively dispersed dissolved gas sensors in transformer oil and wireless temperature measurement systems, the key parameters (temperature, micro water, fault gas concentration, etc.) collected are sent to the nearby LoRa base station through LoRa networking; the base station then uses the 5G network to package and transmit the data to the multi-source data access module or data storage module. This can not only meet the low-power consumption requirements but also provide large bandwidth and high-rate real-time transmission when needed. For on-site TLI-X8 battery online monitoring systems, generator partial discharge online monitoring devices, etc., if wireless transmission is used, the monitoring data is first integrated into lightweight data packets (such as JSON or binary protocol format) through the built-in radio frequency communication unit or external IoT gateway, and a timestamp, device ID, and check code are added. Subsequently, the wireless communication module performs preliminary encryption or authentication processing on the data packet. Since power generation equipment is often distributed in locations such as unit areas, switch rooms, and high-voltage rooms, the internal network environment is complex. In this embodiment, several relay nodes are deployed in the required areas: when the signal of the wireless temperature measurement system or asynchronous motor precision inspection and diagnosis device is weak, it can be relayed to the unified main communication gateway by the relay node and then uploaded to the power plant data center through the IoT operator base station or dedicated 5G base station. In the high-voltage equipment area, the communication module will automatically switch to a communication method with stronger anti-interference ability (such as 5G NR Sub-6G band or LoRa 433MHz band) to ensure accurate data transmission. To prevent external interference and network fluctuations, the wireless communication module adopts an end-to-end encryption strategy and designs a polling mechanism at the gateway end: if transmission anomalies or data packet loss are detected, the system immediately performs retransmission or switches to the backup network. If the network interruption time is too long, the module will temporarily cache the key data in the local Flash storage to ensure data is not lost during the network interruption. The data is uploaded and docked with the monitoring platform. Finally, during the IoT data upload process, the operating parameters and environmental parameters of all on-site devices are centralized to the power plant data center through the wireless channel or forwarded to the cloud operation and maintenance platform through the VPN tunnel. Maintenance personnel can view key indicators such as temperature, negative sequence impedance, and gas content in oil from the wireless communication link on the visualization terminal and conduct comprehensive analysis with the data sources of the wired network to achieve remote and unified supervision of the equipment of the entire plant.

[0056] Data storage module, which is used to store the monitoring data from the multi-source data access module; the data storage module of this embodiment is deployed on the power plant data center server, and its core includes a real-time database and a historical database, which are used for hierarchical storage and unified management of the measurement point information transmitted by the multi-source data access module. In some embodiments, the main process is as follows: when data is uploaded by an asynchronous motor precision preventive maintenance diagnosis device, a transformer oil dissolved gas and micro-water on-line monitoring system, a generator partial discharge on-line monitoring device, etc., the system will first write these newly arrived data into the real-time database (such as a memory-based database or a high-performance time series database), and adopt a cache queue mechanism to achieve fast reading in milliseconds or seconds. Operation and maintenance personnel or application algorithms can directly call these data in the "real-time layer" for fault diagnosis or trend analysis to meet the requirements of equipment on-line status monitoring and high-frequency refreshing. To support long-term traceability and trend research, the data storage module automatically transfers the data in the real-time database to the historical database according to a preset cycle or event trigger rule in the background. The historical database can be based on a relational database (such as PostgreSQL) or a distributed time series database (such as InfluxDB). For example, the three-phase voltage, current, negative sequence current component, fault alarm mark of all motors, as well as the battery voltage, oil gas concentration, temperature sampling value, etc., are marked with a unique timestamp and device identifier and then stored in the historical database for long-term backup. The data storage module automatically parses and cleans the data packets of different formats or protocols from the multi-source data access module: when receiving TLI-X8 battery monitoring data, it will first identify its JSON data structure and map fields such as "battery pack ID", "single cell voltage", "internal resistance" to the database table structure; for transformer oil dissolved gas or partial discharge data, corresponding parsers and feature extraction programs are used to convert the original sampling values into values that can be aligned with other device data. After standardization processing, the data can be retrieved, compared, and analyzed in a unified time series dimension. In this embodiment, to ensure the integrity and traceability of the data, the data storage module maintains multi-level logs: any operations such as adding or deleting device point tables and modifying the monitoring frequency are recorded at the database level with the operator, time, and change content; the data write operations of all key sensors also generate audit records in real time. When a fault backtracking or investigation is required, operation and maintenance personnel can accurately locate the source and write time of each piece of data and retrieve the system configuration at that time. For the fault diagnosis and evaluation module, the visualization human-computer interaction module, or the third-party operation and maintenance management platform, the data storage module of this embodiment provides an open API interface and access permission control: real-time data can be pushed to the fault evaluation algorithm through WebSocket or a lightweight message queue for on-line analysis; historical data is deeply retrieved through SQL or time series query language, which is convenient for generating trend charts, health score reports, or mining the operation rules of the entire life cycle of the equipment.

[0057] Fault diagnosis and evaluation module, the central processing unit is used to analyze the operation parameters and environmental parameter data of the power generation equipment and generate a health evaluation result; the fault diagnosis and evaluation module is deployed in the data center or cloud server of the power plant, and is built-in with a central processing unit (CPU or GPU acceleration) and a variety of intelligent diagnosis algorithms, which are used to comprehensively analyze the operation parameters of the power generation equipment (voltage, current, negative sequence impedance, vibration signal, etc.) and external environmental parameters (temperature, humidity, load condition, etc.), and output a health evaluation result. In some embodiments, the specific process is as follows: Data reception and preprocessing, the real-time or batch data obtained from the multi-source data access module and the data storage module is automatically aggregated to the fault diagnosis and evaluation module through the network interface. The module first aligns the timestamps of the data from different sources and performs necessary interpolation or filtering. For example, if the asynchronous motor precision inspection and diagnosis device transmits the current harmonic component once per second and the temperature sensor uploads at a frequency of once every 5 seconds, then in this module, the key time axis is unified through interpolation to facilitate subsequent algorithm synchronization processing. Feature extraction and fusion, the fault diagnosis and evaluation module adopts corresponding feature extraction strategies for different equipment types: for the stator current of the motor, extract fault frequency features such as negative sequence apparent impedance, stator current modulus spectrum, (1±2s)·f, etc.; for the vibration signal, extract envelope spectrum, signal energy ratio or time-frequency domain joint features; for external environmental data such as temperature, humidity and load, perform regularization or normalization processing for multimodal fusion. When all features are put into the same multi-dimensional data structure, the module can perform further intelligent analysis. In this embodiment, a fault detection algorithm combining a mother convolutional neural network model and a sub-machine learning model is deployed in the central processing unit. The mother network focuses on deep convolution and temporal feature mining of the input data (voltage, current, vibration, etc.); the sub-network adopts structures such as physical constraint neural network (PC-DNN) and dynamic graph attention support vector machine (DGAT-SVM) to carry out more accurate fault discrimination for the multi-element coupling relationship of motors and power plant equipment (such as electromagnetic-mechanical coupling, fault propagation path between equipment). If signs of inter-turn faults in the stator winding are detected, the corresponding fault index, the position of the affected coil and the confidence score are output; if broken rotor bars or abnormal bearing signals are identified, the deterioration trend and possible evolution speed are judged by comparing with historical data. Health evaluation and scoring, after analyzing the fault features, the module further scores and evaluates the health status of the equipment: quantifies the "current health degree" in the form of scores or levels; infers whether the equipment is likely to experience fault escalation in a future period through trend prediction or remaining useful life (RUL) estimation. At the same time, this embodiment provides multi-dimensional evaluation views: single equipment scoring, comparison analysis of the same type of equipment, overall health index of the unit, etc., which is convenient for management personnel to understand the risk distribution at different levels.Diagnostic result output and interaction: When the fault diagnosis and evaluation module detects that the health score is lower than a certain threshold or obvious fault symptoms, it will push the results to the early warning module and the visual human-machine interaction interface. If a sharp deterioration is detected, an emergency alarm will be triggered and immediate shutdown for maintenance is recommended. For minor abnormalities, routine inspection suggestions will be output or the early warning information will be incorporated into the operation and maintenance schedule. The module will also record the details of this diagnosis in the historical database, including fault characteristic components, confidence levels, recommended maintenance times, etc., to support subsequent backtracking and continuous learning and improvement.

[0058] Early warning module: used to generate alarm signals when a fault is detected or the health assessment level is lower than the set threshold. In some embodiments, the early warning module cooperates with the fault diagnosis and evaluation module and is deployed on the application server of the power plant data center. It is mainly used to automatically trigger alarm signals when a power generation equipment fault or the health assessment level is lower than a certain set threshold, so that operation and maintenance personnel can take maintenance measures in a timely manner. Its working process is as follows: Threshold configuration and hierarchical management: Operation and maintenance personnel set the corresponding alarm thresholds and hierarchical strategies in advance in the configuration interface of the early warning module according to equipment type, operation importance, and risk level. For example, when the health score of an asynchronous motor is lower than 60 points, a "warning" level is triggered, and when it is lower than 40 points, an "emergency" level is triggered. When the concentration of certain fault gases in the dissolved gas in the transformer oil continues to climb to a certain value, a high-level alarm will also be directly triggered. Real-time monitoring and judgment: The early warning module receives the health score, fault index, and deterioration trend information output by the fault diagnosis and evaluation module regularly or in real time. When it is identified that the score is significantly lower than the threshold, or the equipment shows serious abnormalities (such as strong characteristics of broken rotor bars, rapid deterioration of insulation status, etc.), the module immediately determines whether the alarm trigger condition is met. For minor abnormalities, they will be recorded in the log and the status will be updated to "to be observed"; for abnormalities exceeding the emergency threshold, the highest-level alarm will be directly activated. Multi-form alarm signal output: Once an alarm is triggered, the early warning module will send notifications to operation and maintenance personnel and managers at different levels in the following multiple ways: Visual interface pop-up window: On the monitoring screen in the control room or management background, a prominent warning message will pop up, indicating the faulty equipment, specific monitoring parameters, and diagnostic suggestions; Mobile terminal push: The details of the fault will be synchronously pushed to the relevant responsible persons via text messages, APPs, WeChat, etc., so that they can understand the abnormalities in a timely manner; Local sound and light alarm: If the on-site equipment supports a local alarm device, the early warning module can also send instructions to trigger the siren or warning light to prompt the inspection personnel to take measures in a timely manner. Alarm handling and closed-loop feedback: After receiving the alarm, the duty personnel or repair team decides whether to shut down for maintenance, further investigate, or perform observational maintenance according to the alarm level and diagnostic results. After the handling is completed, the staff will feedback the corresponding maintenance records or inspection results to the early warning module. This information can be stored together with the historical alarm data for subsequent statistical analysis and model optimization.

[0059] Visual human-machine interaction module, which is used to display the operating status, historical records, alarm information and health assessment results of power generation equipment on the platform interface or mobile terminal. In some embodiments, the visual human-machine interaction module is deployed on the large-screen monitoring system in the power plant central control room and the mobile terminals of operation and maintenance personnel (such as tablets or mobile phone APPs), providing multi-dimensional information display and interaction functions for operation and management personnel. The main implementation steps are as follows: System home page and equipment overview, on the main interface, operation and maintenance personnel can switch to the "Equipment Overview" page with one click, presenting the operating status and health scores of key equipment (such as asynchronous motors, transformers, generators, battery packs, etc.) of the whole plant or each unit. Each device is displayed in the form of a card or icon, and colors or icons are used to indicate whether there is a warning / fault at present - for example, green indicates normal equipment, yellow indicates early warning, and red indicates serious fault. The operator can click on the corresponding icon to enter the detailed monitoring page of the device. Real-time operation data and historical records, Real-time data: For asynchronous motors, display the current voltage, current, negative sequence impedance, slip rate, etc.; for transformer oil monitoring, display the gas concentration and micro water content in the oil; for the wireless temperature measurement system, display the real-time temperature curve, etc. The interface is scrolled and refreshed in the form of a line graph, radar chart or progress bar, and the update period can be customized between 2 seconds and 1 minute. Historical records: The visualization module provides a time axis sliding or calendar selection function, allowing operation and maintenance personnel to retrieve the equipment operation data in the most recent day, week or even longer time, and highlighting key events such as health scores and fault alarm times in the chart with marking symbols to facilitate the analysis of the root cause and evolution process of faults. Visualization and processing of alarm information, when the fault diagnosis and evaluation module of a certain device in the system determines that the health assessment level is lower than the threshold or detects fault symptoms, the early warning information will be reminded through the visual human-machine interaction interface immediately: Pop-up window or warning bar: A distinct warning bar or pop-up window appears at the top of the main interface, listing the faulty equipment, warning level, warning time and specific diagnosis suggestions; Map-based positioning: If the location of the equipment can be displayed on the plant layout diagram, the warning layer will flash the corresponding location icon, and clicking on it can view the detailed fault description and cause analysis; Interactive processing: After the user clicks the "Confirm for processing" button, they can fill in the fault handling measures or remarks information, and the system will save these inputs to the historical database for subsequent auditing and analysis. Health assessment report and trend analysis, the visualization module integrates statistical analysis components, and can automatically generate weekly or monthly reports for the health scores, fault indices and maintenance records of each device. Users can view on the report page: Score change curve: Show the fluctuations of the equipment health score over time, and quickly identify the equipment deterioration trend; Distribution of maintenance and fault events: Such as when alarms occur, when spare parts are replaced or maintenance is performed, forming a closed-loop management; Trend prediction chart: If advanced prediction algorithms are enabled, the system can generate prediction intervals and risk assessment curves to help formulate more scientific maintenance plans.Mobile terminal synchronization and remote operation and maintenance. For the convenience of on-site inspection or remote management, operation and maintenance personnel can log in to the visual human-computer interaction module on the mobile phone APP, tablet computer or Web side: Instant push: If a certain motor fails and alarms, the background immediately pushes relevant information through the APP message, and the inspection personnel can view the fault details on the spot; Remote confirmation and work order dispatching: The "work order dispatching" function on the mobile side allows the operation and maintenance supervisor to directly issue maintenance tasks to designated personnel after seeing the alarm and view the progress at any time.

[0060] Preferably, the asynchronous motor precision inspection and diagnosis device includes: An on-site diagnosis unit, installed inside the motor control cabinet, for collecting three-phase voltage and current signals of the motor and performing real-time fault feature extraction; An asynchronous motor precision inspection communication module, for transmitting the monitoring data and processing results of the on-site diagnosis unit to the data storage module; An asynchronous motor precision inspection local alarm unit, for performing on-site sound and light alarms when motor fault symptoms are detected; The on-site diagnosis unit obtains the three-phase voltage and current of the motor through the secondary side signals of the voltage transformer and current transformer. Under different load and power supply voltage fluctuation conditions, the inter-turn fault of the motor stator winding, the broken rotor bar fault and the bearing fault are identified by the negative sequence apparent impedance filtering value and the stator current modulus spectrum method.

[0061] In some embodiments, the asynchronous motor precision inspection and diagnosis device is applied to the condition monitoring and fault prediction of key auxiliary motors in power plants (such as induced draft fans, forced draft fans, feed water pumps, etc.). This device mainly consists of a local diagnosis unit, an asynchronous motor precision inspection communication module, and an asynchronous motor precision inspection local alarm unit. Combining the secondary side signals of voltage transformers (PT) and current transformers (CT), it realizes high-speed acquisition and fine analysis of the three-phase voltage and current of the motor. The specific description is as follows: Installation and signal acquisition of the local diagnosis unit, Installation location: Place the local diagnosis unit in the empty position of the motor control cabinet. For convenient on-site maintenance and repair, its shell is designed with industrial-grade dust and electromagnetic interference protection. Signal introduction: Through the secondary side wiring of the voltage transformer and current transformer, the diagnosis unit obtains the three-phase voltage and three-phase current of the motor; if a 1A current transformer output is used on-site, a special terminal block is equipped on the secondary side to ensure safe and accurate measurement. Real-time sampling: The high-resolution acquisition card in the diagnosis unit synchronously samples the voltage and current at an adjustable sampling rate (typical value 1 - 2kHz), obtains the instantaneous value and effective value of each phase waveform, and retains the complete fault characteristic frequency band information. Fault feature extraction, Calculation of negative sequence apparent impedance filter value: In each acquisition cycle, the local diagnosis unit first performs phasor decomposition on the three-phase voltage and current signals to obtain positive and negative sequence components, and then extracts the negative sequence apparent impedance characteristics through a specific filter; when this value shows a significant increase or fluctuation relative to the reference, it is initially judged that there may be a stator winding inter-turn fault. Analysis of stator current modulus spectrum: Perform fast Fourier transform (FFT) or higher-resolution spectrum estimation on the three-phase current, extract rotor fault characteristic components such as (1 ± 2s)·f, and record the harmonic amplitude; if a significant increase in amplitude is detected, it indicates a rotor bar breakage fault symptom; at the same time, for the bearing fault scenario, the diagnosis unit will search for the bearing vibration coupling current characteristic frequency in the current spectrum and judge its significance. Asynchronous motor precision inspection communication module, Data packing and uploading: When the diagnosis unit completes each round of fault feature extraction, it generates a data packet containing a timestamp, negative sequence impedance filter value, characteristic spectrum amplitude, and comprehensive health index, and transmits it to the asynchronous motor precision inspection communication module through a local network (such as RS485 or industrial Ethernet). Protocol and security: This communication module supports common industrial protocols such as Modbus TCP / RTU and OPC UA, ensuring fast docking with the upper computer or data storage module; to prevent network impact and external interference, key data fields can also be encrypted or CRC-checked to enhance security and reliability. Asynchronous motor precision inspection local alarm unit, Acoustic and optical alarm principle: When the local diagnosis unit determines that the fault index exceeds the threshold or the health score is lower than the system preset safety threshold, it outputs a trigger signal to the local alarm module. This module drives the buzzer and warning light installed outside the control cabinet to emit acoustic and optical alarms to alert the duty personnel.Alarm Maintenance and Reset: If the device status returns to normal, the diagnosis unit will feedback the "fault cleared" signal to the alarm module to stop the siren or flashing; at the same time, push the alarm cancellation information to the upper-level system to support the background to automatically record the start and end times of this alarm. Under different loads and voltage fluctuations, by comprehensively analyzing the negative-sequence apparent impedance filtering value, stator current modulus spectrum, and harmonic characteristics, this embodiment can perform early perception and classification diagnosis on stator winding inter-turn faults, rotor bar breakage faults, and bearing faults; if a slight anomaly is detected, the system will give an early warning through local audible and visual alarms and pop-up prompts on the remote platform, and the operation and maintenance personnel can arrange targeted inspections or maintenance; if the anomaly is determined continuously for multiple times or a sudden severe fault index occurs, a high-level alarm will be triggered immediately and shutdown for maintenance is recommended to avoid further damage.

[0062] In some embodiments, the obtaining of the operation parameters and environmental parameter data of the power generation device includes: A temperature sensor is attached to the high-voltage switch contact or busbar joint, and the sampling frequency is not less than 1 kHz; A vibration sensor integrates a MEMS gyroscope and an acoustic emission probe to synchronously capture mechanical vibration and micro-crack acoustic wave signals for monitoring the status of the motor bearing; A current transformer is used to collect three-phase current waveform data; A voltage transformer is used to collect three-phase voltage waveform data.

[0063] In some embodiments, in order to fully obtain the key operating parameters and environmental parameters of the power generation equipment, a variety of sensors or mutual inductors are arranged at the high-voltage switch contacts, busbar connectors, motor bodies and internal positions of the control cabinet to collaboratively complete the collection and transmission of temperature, vibration, voltage and current signals. The main implementation steps are as follows: Temperature sensor deployment installation location: Attach the temperature sensor near the high-voltage switch contacts or busbar connectors, and the back of the sensor comes with a high-temperature resistant double-sided adhesive or binding fixing device to ensure stable contact and avoid loosening. Sampling frequency: The temperature sensor is set to a sampling frequency of not less than 1kHz, which is particularly critical when detecting sudden overheating or local temperature rise, and can capture temperature transient information in time. Data upload: After the sensor signal is conditioned locally, it is connected to the multi-source data access module through wired or wireless means, and the temperature data is transmitted to the data storage module in real time or in batches. The vibration sensor integrates a MEMS gyroscope and an acoustic emission probe. Sensor principle: This embodiment uses an integrated sensor to combine a micro-electromechanical system (MEMS) gyroscope with an acoustic emission probe, which can simultaneously detect mechanical vibration and micro-crack acoustic wave signals. Installation and calibration: The sensor is installed on the motor bearing end cover or base through a magnetic base or bolt fixing method. The sensor and the measuring point position remain relatively fixed, and a calibration test is performed before the system goes online. Fault monitoring: When the bearing has surface peeling or raceway microcracks, the acoustic emission probe can capture high-frequency sound waves, and the MEMS gyroscope can detect the micro-amplitude and spectrum characteristics of the vibration, providing key raw data for the subsequent fault diagnosis and evaluation module. The current transformer collects three-phase current waveform data. CT specification selection: Select a suitable current transformer (CT) according to the motor power and rated current range, and the secondary side standard output is 1A. Installation and secondary side safety: The primary side of the CT passes through the motor power line or busbar, and the secondary side is connected to the multi-source data access module or the asynchronous motor precision inspection and diagnosis device through a dedicated terminal block. Considering safety and interference factors, the secondary circuit is equipped with short-circuit terminals and shielding grounding measures. Real-time waveform acquisition: During the operation of the motor, the current waveform output by the CT that changes with the load is sampled at high speed and used for subsequent negative sequence apparent impedance calculation, rotor broken bar spectrum detection and other analysis links. The voltage transformer collects three-phase voltage waveform data. Wiring form: A voltage transformer (PT) is configured in the power distribution circuit to convert high voltage of several thousand volts into a secondary signal of hundreds of volts or a lower voltage range, which is convenient for safe collection and digital processing. Waveform recording: The multi-source data access module is combined with the local diagnostic unit or data storage module for joint collection to obtain the waveform of each phase voltage changing over time, including information such as grid harmonics and unbalanced components. Linkage analysis: When the voltage transformer and current transformer signals are sampled synchronously, the power, power factor, negative sequence current and negative sequence voltage of the power generation equipment can be obtained with the help of vector calculation, which is used to monitor the motor working condition in real time.

[0064] Preferably, the data storage module comprises: A real-time database for storing the monitoring data of the most recent time or within a continuous short period and supporting high-frequency queries; A historical database for storing long-term monitoring data in a time series to support trend analysis, fault tracing, and health modeling; A data interface management unit for receiving the raw data from the multi-source data access module, performing standardized processing on it, and writing it into the database.

[0065] In some embodiments, the data storage module is deployed on the servers of the power plant data center, aiming to meet the requirements of fast query for real-time high-frequency data and long-term traceability analysis for historical massive data simultaneously. The main implementation process is as follows: High-frequency storage and query of the real-time database, Data storage: When the multi-source data access module detects new monitoring data (such as three-phase current waveforms of asynchronous motors, gas concentrations in transformer oil, temperature, etc.), it will quickly write it into the real-time database through the network interface. Since the real-time database is usually based on memory or high-performance time series structures, it can receive and store data at a frequency of seconds or even milliseconds. High-speed query: When the fault diagnosis and evaluation module or the visual human-machine interaction interface needs to obtain the current state of the device, the system will directly retrieve the latest sampling values or data in the recent few minutes / seconds from the real-time database, realizing instant report and chart display of high-frequency dynamic changes. This can quickly locate problems and conduct preliminary judgments when anomalies are found. Long-term data maintenance of the historical database, Segmentation and archiving mechanism: The data retained in the real-time database usually only covers the records of the most recent time or a short time window (such as half an hour or several hours). To prevent overload of the real-time layer, the module will archive the full or expired data to the historical database at a pre-set cycle (for example, every 15 minutes or 1 hour). Time series retrieval and multi-dimensional analysis: The historical database is based on a time series form or a distributed storage structure, indexing the monitoring data by device number, timestamp, and data type. Maintenance personnel or data scientists can call the API or dedicated query statements to conduct in-depth retrieval according to the time range, device ID, or fault label, supporting applications such as trend analysis, fault tracing, model training, and health modeling. Capacity elastic expansion: When more monitoring devices are connected to the power plant or the data scale continues to grow, the historical database can be flexibly expanded by adding nodes or storage shards to ensure that the read and write efficiency is not affected. Standardized processing of the data interface management unit, Protocol parsing and format conversion: Among the data transmitted by the multi-source data access module, there may be messages in Modbus TCP, OPCUA, or private protocol formats. The data interface management unit parses it, performs field mapping and data type conversion, for example, converting the sensor JSON fields into a unified key-value pair structure and indexing by timestamp. Data cleaning and quality control: For occasional data packet loss, invalid values, or abnormal peaks, the interface management unit can adopt strategies such as interpolation, filtering, or elimination to ensure high-quality information finally written into the database. Before writing into the historical database, additional tags (such as "fault event", "alarm confirmation") may also be added for subsequent retrieval. Access permission management: When different users or application systems call data, the interface management unit decides whether to allow access or perform partial desensitization processing according to the role or security policy, preventing unauthorized access or modification of important parameters.

[0066] In some embodiments, the multi-source data access module integrates data in the following ways: Align the battery monitoring data using time series alignment; Adopt spectral analysis technology for the dissolved gas data in transformer oil; Perform high-frequency signal filtering and feature extraction on the partial discharge data.

[0067] In some embodiments, the multi-source data access module needs to integrate multiple monitoring systems with different types, sampling frequencies, and data formats. To ensure accurate data alignment and usability, the following specific measures are taken: Time series alignment of battery monitoring data. Data source: The TLI-X8 battery online monitoring system periodically outputs key parameters such as battery voltage, current, and internal resistance of each cell, and uploads them to the multi-source data access module in JSON or CSV format through a gateway. Timestamp parsing: Since the battery monitoring system and the multi-source data access module may use different clock sources or time precisions, after receiving the data, the access module first reads the timestamps carried by each record and converts them into a unified UTC format or a standard time axis based on the factory time zone. Sequence alignment: In this embodiment, interpolation or zero-padding is used to align each battery data point on the time axis. For example, if the system reads battery data at 30-second intervals and the data refresh frequency of other monitoring devices is higher, new time nodes that are not collected are filled by interpolation to ensure that the time axes of each record are consistent when performing correlation analysis or visualization with other monitoring data in the future. Spectral analysis of dissolved gases in transformer oil. Raw data acquisition: The TRANSFIX™ online monitoring system outputs gas concentration in oil, micro-water content, and raw photoacoustic spectroscopy measurement data at regular intervals. The multi-source data access module directly captures these concentration values and spectral curves using a communication protocol (such as Modbus or OPC UA) that matches the system. Spectral analysis and processing: A spectral analysis algorithm is pre-built into the access module to split and extract the absorption peaks of multiple fault gases in the oil (such as key gases like CO, C2H2, CH4, etc.), and automatically identify whether the content of the fault gas exceeds the safety threshold. Data standardization: The analysis results and concentration values are stored in the same format by field, or only the main indicators such as dissolved gas content and spectral peak positions are extracted and written into the database, so that the transformer oil monitoring data can be synchronously displayed with other device parameters or used for comprehensive diagnosis. High-frequency signal filtering and feature extraction of partial discharge data. Data sampling: Generator partial discharge online monitoring devices (such as W-PD6) often acquire discharge pulse waveforms at a high sampling rate and obtain the partial discharge intensity (PDI) or high-frequency signal envelope through pre-processing. High-frequency signal filtering: The multi-source data access module performs denoising and band-pass filtering on the high-frequency pulse envelope from the generator partial discharge online monitoring device to remove power frequency and low-frequency interference and extract the pulse components truly related to the discharge. Feature extraction: Based on the filtered results, the module uses a feature extraction algorithm to calculate key parameters such as pulse amplitude, pulse interval, and cumulative energy; if abnormal values (such as continuous increase in pulse intensity) continuously appear, they are also marked as "suspected discharge fault" at this stage and passed to the fault diagnosis and evaluation module or directly trigger an alarm.

[0068] Preferably, the fault diagnosis and evaluation module includes: Using the mother convolutional neural network model, feature extraction calculations are performed on the input vectors composed of historical and real-time input voltage, current, negative sequence impedance, harmonics, temperature, and motor load conditions to improve the sensitivity and robustness of fault diagnosis; The data input into the mother convolutional neural network model also includes the feature data obtained by calculating the fault diagnosis algorithm library.

[0069] In some embodiments, the fault diagnosis evaluation module uses the mother convolutional neural network (hereinafter referred to as the mother CNN) to perform deep feature extraction on the multi-source data of the motor and its environment, and combines the feature data output by the fault diagnosis algorithm library to achieve high-sensitivity online detection of multiple types of faults. Its system structure and operation principle can be described as follows: Input data organization and preprocessing, multi-dimensional input vector: The input of the mother CNN is jointly composed of (voltage, current, negative sequence impedance, harmonic components, temperature, motor load conditions) and "feature data obtained by calculating the fault diagnosis algorithm library". Time window segmentation: The monitored data obtained in real time or stored historically is segmented according to a time window of a fixed length (such as 1 second or 5 seconds), and the signals of each channel are normalized or standardized within the window to make the input features in a similar numerical range, which is convenient for convolutional operations. Multi-channel splicing: If the original acquisition is N-channel signals (such as three-phase voltage, current, temperature, load, etc.) plus k features extracted by the algorithm library (such as negative sequence impedance filter value, harmonic amplitude, etc.), in this embodiment, all channels are spliced into a multi-channel tensor in the "channel dimension", in the form of batch size, number of channels, time step, to adapt to the input layer format of the CNN.

[0070] Structure of the mother convolutional neural network model, First convolutional layer (Conv1): The size of the convolutional kernel is set to 3×1 or 5×1, aiming to capture local features on the time series axis; the number of channels is set to 16 or 32 filters, and the activation function can be selected from ReLU or an improved activation function, making the model highly sensitive to instantaneous changes in voltage and current. Second convolutional layer (Conv2): After initially extracting the underlying features, the second convolution expands the receptive field and sets a larger convolutional kernel (such as 5×1) to capture the change patterns of fault harmonics over a longer time interval; the number of channels is increased to 64 filters; during training, the network stability is improved through batch normalization. Pooling and skip connections: After each convolutional layer, the mother CNN can insert a max pooling or average pooling layer to reduce the data dimension; skip connections can also be set between layers to make the network more likely to retain key detailed features and avoid gradient vanishing. Third convolutional layer (Conv3): Further aggregate multi-channel information, and deeply mine high-frequency harmonics, negative sequence current fluctuations, and temperature-load coupling changes; after this layer, a Flatten operation is performed to expand the convolutional output into a one-dimensional feature vector for integration by the fully connected layer. Fully connected layer: The Flattened feature vector is input into one or two fully connected layers for comprehensive discrimination; this part can output one or more fault scores (such as stator winding inter-turn fault score, rotor bar breakage score, bearing fault score), or they can be combined into a comprehensive health score.

[0071] In this embodiment, in addition to the original waveforms (voltage, current, temperature, load, etc.), the input of the mother CNN also includes feature data calculated by the fault diagnosis algorithm library, such as traditional diagnosis indicators like negative sequence current component, (1±2s)·f harmonic amplitude, etc.; spectral center frequency, vibration characteristic quantities, etc. These prior features can complement the local patterns extracted by CNN deep learning, improving the discrimination ability and response speed of the model for various fault types. Training and operation process Training stage: Using the labeled normal or fault samples in the historical data, after splitting them by time window, input them into the mother CNN; use stochastic gradient descent (SGD), Adam or other optimization algorithms for iterative training, and monitor the accuracy, error, and overfitting situation on the validation set; if a physics-constrained neural network or other sub-models are introduced, they can also be jointly or stagewise trained during this period. Online operation stage: Once the real-time data is read in, the multi-source data access module and the data storage module first perform preprocessing; the fault diagnosis and evaluation module inputs the newly arrived data block (including prior features) into the mother CNN for forward calculation to obtain the fault score or health status; if the score is lower than the safety threshold, or there is a significant increase in fault features, the warning module is triggered to remind the operation and maintenance personnel.

[0072] Through the deep convolutional learning of multi-channel data and prior features, the mother CNN can still extract relatively stable and sensitive feature components even when there is noise and fluctuation in the asynchronous motor and its environmental parameters; when there are significant differences between historical environmental conditions and current load conditions, the CNN can maintain good fault recognition robustness under multiple working conditions by virtue of hierarchical feature fusion and skip connections; combined with the traditional feature data obtained from the fault diagnosis algorithm library, it further enhances the convergence speed and accuracy in the initial stage of the network, reduces the dependence on large-scale pure data-driven, and forms a double guarantee of "deep learning + domain prior knowledge". Through this embodiment, the mother convolutional neural network model can more effectively mine the potential correlations and fault signal patterns of multi-dimensional data (electrical, temperature, harmonic, negative sequence impedance, etc.) of power generation equipment, greatly improve the diagnostic sensitivity and anti-interference ability, and provide more accurate technical support for the early fault detection of key equipment and subsequent operation and maintenance decisions.

[0073] Preferably, the fault diagnosis algorithm library is based on the following methods: For the inter-turn fault of the stator winding, the negative sequence current component analysis is used to generate the first analysis data; For the broken rotor bar fault, the detection of the (1±2s)*f frequency component in the stator current spectrum is used to generate the detection data; where s represents the slip ratio of the asynchronous motor and f is the fundamental frequency of the power supply; For the bearing fault, the joint time-frequency domain analysis of the vibration signal is used to generate the second analysis data.

[0074] In some embodiments, the fault diagnosis algorithm library pre-collects the analysis methods and feature extraction processes required for the main fault types of the motor, and can provide prior feature information for the fault diagnosis evaluation module based on the real-time or off-line monitoring data of the motor. The specific description is as follows: Analysis of stator winding inter-turn faults, data source: The three-phase current signals collected at high speed in the precision spot-check diagnosis device of the asynchronous motor are uploaded to the fault diagnosis algorithm library through the multi-source data access module. Calculation of negative-sequence current component: The algorithm library first performs phasor decomposition on the three-phase current to separate the positive-sequence, negative-sequence, and zero-sequence components, and focuses on the amplitude and phase angle of the negative-sequence component. Fault feature recognition: If the negative-sequence current component shows a significant increase relative to the reference value or shows a continuous over-limit trend, it is determined that there is a potential risk of stator winding inter-turn short circuit, and the first analysis data (including negative-sequence amplitude, change rate, and fault index, etc.) is generated. This analysis result is then packaged and transmitted to the fault diagnosis evaluation module for input to the mother convolutional neural network model or directly triggering an alarm. Analysis of rotor broken bar faults, frequency component detection: Using the fast Fourier transform (FFT) or other high-resolution spectral estimation methods, the spectrum analysis of the three-phase stator current waveform is carried out, and special attention is paid to the harmonic amplitude at the characteristic frequencies such as (1±2s)*f. Calculation of slip ratio: Under the actual operating conditions of the motor, the slip ratio s can be estimated from parameters such as load torque and speed; the algorithm library substitutes it into (1±2s)*f to accurately locate the fault harmonics.

[0075] Fault indication output: If the harmonic amplitude exceeds the normal reference and shows a continuous increase, it is regarded as a sign of rotor broken bar fault, and the corresponding detection data is generated. The algorithm library will record the fault factor, amplification multiple, and detection timestamp, and provide this information to the fault diagnosis evaluation module or the data storage module to further confirm the fault level in combination with other dimensional features. Time-frequency domain joint analysis of bearing faults, vibration and acoustic emission data: The vibration sensors or acoustic emission probes arranged at the bearing part of the motor will capture the mechanical vibration waveform and micro-crack noise signal at a high sampling rate. Time-frequency domain fusion: The algorithm library performs joint analysis of the vibration signal between the time domain and the frequency domain through methods such as short-time Fourier transform (STFT) or wavelet transform, and extracts characteristic values such as envelope spectrum, pulse interval, and energy entropy; in addition, the peak value and main frequency band of the acoustic emission signal can be statistically analyzed. Generation of the second analysis data: If typical bearing fault characteristic frequency peaks or high-amplitude pulses appear in the detected vibration envelope spectrum, the algorithm library will determine the risk of bearing surface spalling or raceway cracks, and output the second analysis data (bearing fault frequency, pulse intensity, cumulative damage index, etc.), which, after being fused with other electrical parameters, provides key input for the mother CNN model or other sub-models.

[0076] Through the above implementation process, the fault diagnosis algorithm library specifically extracts core feature components when detecting three common motor fault types (stator winding turn-to-turn, rotor bar breakage, and bearing damage), and summarizes the results into a unified feature data format for subsequent mother convolutional neural network models or other fault diagnosis and evaluation algorithms to make comprehensive judgments. This combination of "algorithm library prior knowledge + deep learning" significantly improves the detection sensitivity and judgment accuracy of various motor faults.

[0077] Preferably, the early warning module includes three levels: real-time alarm, early warning and prompt information, which correspond to different processing strategies for emergency situations, suspected faults and normal states. In the early warning module of the present invention, three types of health level thresholds are set: when the fault score is greater than 0.8, it is defined as an "emergency situation", and the system immediately triggers a real-time alarm, including local sound and light warnings, mobile terminal push and digital twin interface highlight flashing, and the linkage protection strategy automatically reduces the load or shuts down the power; when the score is between 0.5 and 0.8, it is defined as a "suspected fault", and the system outputs early warning information, prompting that the equipment may have early hidden dangers, and it is recommended to arrange manual inspections in the near future, calibrate sensors or enter the key monitoring queue; when the score is lower than 0.5, it is regarded as a "normal state", and the system generates prompt information, which is only recorded in the log and updates the status panel without intervention. The above strategy combines the fault diagnosis score and its trend change, realizes classification judgment in the early warning module through the threshold judgment logic, and generates corresponding processing suggestions in conjunction with the operation and maintenance strategy library, realizing intelligent response to different health states.

[0078] Preferably, the visual human-computer interaction module also includes a digital twin module, which displays the equipment operation status in real time through three-dimensional modeling and simulates the fault evolution process.

[0079] Preferably, the digital twin module supports the following functions: Dynamic visualization of equipment configuration; Infrared thermal imaging and real-time data superposition display; Historical fault case library matching and recommended maintenance strategies.

[0080] In some embodiments, in order to enable operation and maintenance personnel to more intuitively and comprehensively grasp the real-time operating status and potential fault evolution process of power generation equipment, a digital twin module is added to the visual human-computer interaction module, and on this basis, functions such as dynamic visualization of equipment configuration, infrared thermal imaging overlay display, and matching with the historical fault case library are expanded. The main implementation process is as follows: Construction and initialization of the digital twin model, 3D modeling: According to the actual structure of the power plant equipment, a virtual model is generated using CAD drawings, 3D scanning, or professional modeling software (such as SolidWorks, 3ds Max, etc.), including asynchronous motors, transformers, generator bodies, busbars, and key connection components. Model parameter initialization: Combining the equipment nameplate information, rated parameters, dimensions, and spatial coordinates in the data storage module, the virtual model is corresponding to the actual equipment one by one to ensure that the size and position of each component in the digital twin environment are highly consistent. Communication interface: The digital twin module exchanges data with the multi-source data access module or the fault diagnosis and evaluation module through the visual human-computer interaction interface to achieve real-time mapping of the current operating parameters (temperature, current, voltage, etc.) of the equipment. Dynamic visualization of equipment configuration, model dynamic drive: On the digital twin platform, each equipment object (such as a motor, transformer) is equipped with several visualization components (rotor, bearing, terminal, etc.). When the actual operating parameters change, the system will correspondingly adjust the color, rotation speed animation, or transparency in the model. For example, if the negative-sequence apparent impedance of the motor increases, the winding color displayed on the model will gradually change to give a reminder. Interactive configuration management: Operation and maintenance personnel can select a certain piece of equipment in the interface, expand the component tree structure to view its logical relationship and real-time values, and dynamically add or remove some sensor layers. In this way, the equipment structure, operation data, and monitoring point layout can be clearly seen at a glance. Infrared thermal imaging and real-time data overlay display, Infrared thermal imaging data acquisition: If an infrared thermal imager or infrared temperature measurement module is equipped on-site, the captured infrared image stream can be mapped to the digital twin model according to the calibrated coordinate system. Visualization fusion: When the system receives a new infrared image, a chromatogram distribution map is immediately generated and overlaid with the virtual 3D model to present the hot spot area of the equipment surface temperature. If the local temperature of some electrical contacts is too high, it will be prominently marked on the model (for example, in the form of a flashing red color or a hot zone diffusion animation). Data linkage: Any high-temperature area found in the thermal imaging will also automatically match the corresponding sensor temperature record (such as the data of the wireless temperature measurement module) to facilitate comparison and verification of whether it has exceeded the safety threshold. Matching with the historical fault case library and recommending maintenance strategies, Case library retrieval: When the digital twin module identifies a certain fault symptom (such as abnormal temperature of the motor bearing or a sharp increase in the partial discharge signal), it will perform similarity matching according to the built-in historical fault case library in the system (including the recorded fault occurrence time, fault mode, repair plan, etc.).Recommended maintenance strategy: If the system retrieves similar historical cases, and the handling measures of the case successfully prevented the accident or reduced the loss, the digital twin module will pop up a "Maintenance Recommendation" window in the visual interface, showing possible root causes of the fault, priority inspection parts, estimated maintenance hours and spare parts requirements; operation and maintenance personnel can quickly formulate or adjust maintenance plans based on this. Evolution process simulation: Digital twins can also simulate the diffusion process of faults within the equipment structure. For example, if the rolling elements of the bearing continue to wear more, the model will show the evolution trend of vibration energy or temperature zone frame by frame to help evaluate the serious consequences that may be caused by delayed faults.

[0081] Preferably, it also includes a data interface of the SCADA system to achieve seamless integration of partial discharge intensity PDI signals and temperature data. The data interface of the SCADA system adopts a standard industrial communication protocol (such as Modbus TCP, IEC 61850 or OPCUA), and realizes data docking through a communication gateway deployed between the monitoring master station and the generator partial discharge online monitoring device. The monitoring master station is located at the core of the enterprise LAN. It is both a data center and an interactive entrance for operation and maintenance personnel, and provides a summary interface for a higher layer (industry cloud platform). The generator partial discharge online monitoring device can upload the real-time collected PDI signal and the synchronously measured high-voltage contact temperature data in the form of periodic messages. The SCADA system parses the above monitoring data and incorporates it into a unified scheduling platform by configuring a unified data point table and variable mapping relationship. In terms of system structure, the multi-source data access module serves as an intermediate layer to realize the preprocessing of PDI values ​​and temperature values ​​(such as denoising, completion, and timestamp alignment), and then writes them synchronously into the SCADA database. In order to ensure the real-time and consistency of the integration process, the system sets up a data cache and synchronization mechanism to ensure that PDI and temperature data can be displayed without delay on the SCADA interface, and trigger alarms or linkage strategies, so as to achieve seamless integration and unified monitoring. The data cache and synchronization mechanism process is as follows: The cache-synchronization process adopts the "sequence number + cursor + ACK" mechanism: the field gateway first writes each monitoring message into the circular memory buffer, and automatically attaches a millisecond timestamp and an auto-increment sequence number. The synchronization thread batches out the unsent segments every 500ms, packages them and pushes them to the central server via MQTT or OPC UA. After receiving it, the server returns ACK according to the sequence number, and the gateway slides the cursor accordingly to dequeue the confirmed segment; if ACK is not obtained within 5s, it enters the retransmission queue and records the retry count. The cursor is written to the local Flash every minute, and it can be retransmitted according to the latest sequence number after power failure. When the link switches between LoRa / 5G, the synchronization thread reads the cursor and continues to send to avoid duplication or packet loss. If the center detects a gap in the sequence number, it will actively initiate a request to the gateway to make up for it and verify the CRC; if there is a conflict in the same sequence number, the one with the latest timestamp will prevail to achieve data consistency.

[0082] Preferably, the asynchronous motor precision spot check diagnosis comprises the following steps: Collect the stator current, voltage and vibration signals of the motor in real time; Extract the negative sequence impedance characteristics and spectral characteristics; Judge the risk levels of inter-turn short circuit, rotor bar breakage and bearing faults through sub-machine learning models; Generate maintenance suggestions and push them to the user terminal.

[0083] In some embodiments, for the daily operation and fault prediction requirements of on-site asynchronous motors, a precise inspection and diagnosis process for asynchronous motors is deployed in the power plant environment, which specifically includes the following steps: Real-time acquisition of motor stator current, voltage, and vibration signals. Signal source: An in-situ diagnosis unit installed in the motor control cabinet collects three-phase current and voltage through the secondary sides of current transformers (CTs) and voltage transformers (PTs); vibration sensors are arranged at the bearing parts to monitor mechanical vibration waveforms in real time. Sampling control: The system determines the sampling frequency (such as 1 kHz to 2 kHz) according to the load size and process requirements. After digitizing the collected waveform data and attaching a timestamp, it is uploaded to the precise inspection and diagnosis device or the multi-source data access module. Extract negative sequence impedance characteristics and spectral characteristics. Negative sequence impedance calculation: Perform positive sequence and negative sequence decomposition on the three-phase voltage and current vectors, extract the negative sequence voltage and negative sequence current components, and then calculate the negative sequence apparent impedance; if it is observed that the negative sequence impedance increases abnormally or fluctuates beyond the safety threshold, it indicates a risk of inter-turn fault in the motor stator winding. Spectral characteristic acquisition: Perform fast Fourier transform (FFT) or other high-resolution spectral estimation on the current and vibration waveforms, and pay attention to characteristic harmonics such as (1 ± 2s)·f (where s is the slip ratio and f is the fundamental frequency), as well as peaks or envelope spectrum anomalies caused by common bearing fault frequencies. This spectral characteristic will be an important basis for fault identification. Judge the risk levels of inter-turn short circuit, rotor broken bar, and bearing faults through a sub-machine learning model. Multi-modal input: Input the negative sequence impedance value, spectral characteristics, and vibration signal parameters (such as acceleration RMS, main vibration frequency band energy, etc.) into the sub-machine learning model. If environmental data (such as temperature, load change) and historical maintenance records are also combined, a more comprehensive assessment of the motor health status can be carried out. Intelligent analysis: The sub-machine learning model can adopt methods such as deep neural networks, support vector machines, or physical constraint neural networks (PC-DNN) to achieve classification or grading judgment of fault types and severities. Output fault indices or risk scores for inter-turn short circuit of the stator winding, rotor broken bar, and bearing wear respectively, and retain a certain confidence interval to improve the accuracy and interpretability of the judgment. Generate maintenance suggestions and push them to the user terminal. Maintenance suggestion generation: If the risk score of a certain fault exceeds the preset threshold, or the health score drops significantly, the sub-model will automatically generate maintenance or inspection suggestions according to the pre-defined maintenance strategy library. The content includes information such as the suspected fault location, risk persistence, and when to perform a shutdown inspection or replace vulnerable parts. Push and closed-loop management: The system conveys the above maintenance suggestions to on-site operation and maintenance personnel and management immediately through a visual human-machine interaction module or a mobile APP. The operation and maintenance personnel can feedback the final inspection results, spare part replacement situations, etc. to the system to form a complete closed-loop management and fault evolution file.Through the above steps, this embodiment realizes the real-time collection and intelligent analysis of the electrical parameters and vibration information of the motor, can quickly give early warnings and provide maintenance decisions in the early stages of stator winding inter-turn, rotor bar breakage, and bearing failures, significantly reducing the probability of major overhauls and the risk of unplanned outages, and improving the safety and reliability of power generation equipment.

[0084] In some embodiments, the sub-machine learning models generally adopt a system of series-parallel combination of multiple modules, covering parts such as "Physics-Constrained Deep Neural Network (PC-DNN)", "Dynamic Graph Attention Support Vector Machine (DGAT-SVM)", and "Cross-Domain Incremental Learning Unit". The core idea is to integrate the classification / regression methods of traditional machine learning with the physical equations of the motor, dynamic graph topology, and incremental learning, and still maintain high precision and high robustness under complex working conditions. The following is a brief introduction to each sub-module: PC-DNN (Physics-Constrained Deep Neural Network), embedding physical priors such as the electromagnetic-mechanical coupling equations of asynchronous motors (such as Maxwell's equations, Hertz contact theory) in the deep neural network structure. Through the "physical loss function" or "differentiable operator", the network output is forced to satisfy the constraints of some device real mechanisms, reducing the dependence on large-scale labeled data and avoiding predictions that violate physical laws. DGAT-SVM (Dynamic Graph Attention Support Vector Machine), modeling the device relationship as a dynamic graph structure, where nodes represent each device or key subsystem, and edges represent fault associations or energy couplings. The attention mechanism is introduced to focus on the most representative edge or node subset, and then the support vector machine (SVM) is used to classify or regressively score the fault risks of the nodes (devices). By dynamically updating the edge weights, the model can adaptively adjust with changes in the operating state and historical fault propagation information. Cross-Domain Incremental Learning and Uncertainty Quantification, using technologies such as adversarial domain adaptation (ADA) or online knowledge distillation to solve the distribution differences between simulation data and measured on-site data, and supporting rapid adaptive learning when new working conditions or new devices are put into operation. The output confidence is quantified through Monte Carlo Dropout or fuzzy membership functions, providing a confidence interval or deviation degree for fault judgment for operation and maintenance personnel.

[0085] In some embodiments, for multimodal input fusion, raw data such as motor voltage, current, vibration, temperature, etc. are obtained from the multi-source data access module, as well as eigenvalue (such as negative sequence impedance, harmonic indexes such as (1±2s)·f, etc.) from the fault diagnosis algorithm library. If environmental parameters (load, humidity, ambient temperature, etc.) or historical fault case matching information are also included, they are incorporated into the input vector and handed over to the sub-machine learning model after preprocessing. Combining physical constraints with deep learning, the PC-DNN part first performs multi-layer perception and convolution operations on the multimodal input to extract potential fault features; at the same time, physical constraint units are added to the network, such as discretizing Maxwell's equations into differentiable operators to correct the coupling of stator electromagnetic distribution and rotor faults; in the bearing fault branch, the roller-raceway stress distribution in Hertz contact theory is used as prior knowledge to inject into the convolution kernel weights or regularization terms, forcing the network output to conform to physical laws. For the dynamic graph attention mechanism, in the scenario of multi-device or multi-system coupling (such as a fault in one motor may affect other devices), the DGAT-SVM part abstracts these devices / systems into a dynamic graph; the node features can include vibration, current harmonic distortion rate, temperature gradient, etc., and the edge weights are updated based on the historical fault propagation probability or electrical connectivity. The attention mechanism automatically evaluates the importance of each node and edge, focuses on the critical paths that may trigger cascading faults, and then uses a support vector machine to classify or regressively score the node-level or system-level risks. For cross-domain incremental learning and uncertainty assessment, when new working conditions, new devices or new fault modes appear, the cross-domain incremental learning module uses adversarial domain adaptation (ADA) technology to reduce the difference between the on-site real data distribution and the existing simulation data; at the same time, online knowledge distillation integrates new knowledge with the existing model, enabling adaptation to scenario changes without large-scale retraining. The uncertainty quantification engine uses methods such as Monte Carlo Dropout and fuzzy membership degree at the model output layer or the DGAT-SVM decision boundary to provide confidence intervals and fuzzy measures for the fault probability or health score, helping the operation and maintenance personnel evaluate the reliability of the diagnosis results. Output the fault risk level and maintenance suggestions. After the above sub-model processes, each motor or other device obtains one or more fault risk scores (such as stator fault score, rotor fault score, bearing fault score), and a comprehensive health index; if the risk score exceeds the threshold, a warning is triggered, and the maintenance strategy library is called to generate more specific maintenance plans or spare part lists, and at the same time, the diagnosis details and confidence are pushed to the visual human-machine interaction module.

[0086] In some embodiments, the processing flow of the sub-machine learning model is as follows: Data acquisition: Obtain real-time / historical data + prior features in the algorithm library; Feature preprocessing: Normalization, sliding window segmentation, or high-low frequency stratification; PC-DNN analysis: Deep network + physical equation constraints, output intermediate fault features; DGAT-SVM analysis (optional, for multi-device coupling scenarios): Construct a dynamic graph, focus attention on key nodes and edges, and SVM obtains device or system-level fault risks; Incremental learning and uncertainty quantification: If there are new working conditions or fault modes, perform adversarial domain adaptation or online distillation, and calculate the output confidence interval; Result release and maintenance strategy recommendation: If the fault score exceeds the limit, push early warnings and maintenance plans; Otherwise, continue to accumulate data for health monitoring. Fusion of physical mechanisms: PC-DNN avoids blind data-driven, improves generalization ability and credibility in small-sample environments; Dynamic fault association: DGAT-SVM can identify the coupling fault propagation paths between multiple devices, truly realizing system-level diagnosis; Continuous evolution: Cross-domain incremental learning can quickly adapt to changes in working conditions or the access of new devices without large-scale offline retraining; Reliable output: Uncertainty quantification provides a risk buffer for operation and maintenance decisions, helps managers understand the diagnostic confidence, and reduces losses caused by misjudgment.

[0087] In some embodiments, the sub-machine learning model adopts a hybrid architecture of a deep neural network and a support vector machine. The sub-machine learning model adopts a hybrid architecture of a deep neural network (DNN) and a support vector machine (SVM) to fully combine the feature automatic extraction ability of deep learning and the robust performance of SVM in small-sample and high-dimensional classification problems. This hybrid model is for the fault diagnosis scenarios of asynchronous motors and other key power generation equipment, and the specific implementation steps are as follows: Deep neural network (DNN) feature extraction, Input layer: The feature vectors generated by the multi-source data access module or the fault diagnosis algorithm library (such as three-phase voltage and current of the motor, ambient temperature, vibration signal statistics, negative sequence impedance, harmonic amplitude, etc.) are summarized into an input matrix or tensor in a unified format. Hidden layer design: DNN usually consists of several fully connected hidden layers, each layer is equipped with a non-linear activation function such as ReLU or LeakyReLU, and batch normalization can be added to stabilize the training process. Feature dimensionality reduction and fusion: If the input data dimension is too high, a dimensionality reduction layer (such as a fully connected layer + Dropout) or an autoencoder structure can be placed after some hidden layers to compress and fuse multi-modal information, remove redundant features and extract the main information in high dimensions. High-order expression output: After the last hidden layer ends, DNN outputs an intermediate feature vector, usually called a "deep representation" or "embedding vector", which contains the abstract patterns and potential associations of the original data. Support vector machine (SVM) classification / regression, Receive the DNN output: Input the high-order feature vector extracted by the above DNN into the SVM classifier or regressor. Since DNN has fully learned the features of the noise and non-linear parts of the original data, SVM can perform efficient classification or regression in a relatively compact feature space. Kernel function and parameter selection: For the recognition requirements of multiple motor fault types, the RBF kernel (radial basis kernel) or polynomial kernel can be selected. If the sample set contains multiple fault categories, a multi-classification strategy (such as One-vs-Rest or One-vs-One) is used, and the discrimination results are trained and fused separately between each fault class and the healthy state. Hyperparameter training: Use historical fault data and normal condition data for training and verification, and optimize the penalty coefficient C, kernel function parameter γ, etc. of SVM by means of grid search or genetic algorithm to ensure stable and high-precision classification boundaries even under small samples or complex non-linear conditions. Overall hybrid process training stage: First, pre-train DNN to enable it to initially learn the ability to extract motor state features; with the DNN weights frozen or fine-tuned, input the intermediate feature vector into the SVM classifier / regressor to continue training, and finally realize fault category recognition or health score regression.Online inference stage: Multidimensional sensor data of motors or other devices are obtained in real time; DNN performs forward calculation to output a depth feature vector; this depth vector is input into SVM to complete the determination of fault types, and the abnormal level or health score is output; if a high-risk fault is identified or the score is significantly lower than the threshold, the warning module is triggered and maintenance suggestions are pushed to the operation and maintenance personnel. Advantages and application values, efficient feature extraction: DNN can automatically learn highly distinguishable non-linear features from the original data (voltage, current waveform, vibration time-frequency analysis values, etc.), complementing complex patterns that are difficult to cover by pure manual features; robust discrimination ability: SVM constructs an optimal separation hyperplane using kernel functions in a high-dimensional space, which can maintain good discrimination ability in the presence of noise and local outliers and reduce the risk of overfitting; flexible combination: in practical applications, according to the hardware resources and fault type requirements, the number of DNN layers, the number of neurons, and the form of the SVM kernel function can be adjusted independently, making this hybrid architecture applicable to both large-scale historical data and small samples or sudden fault situations on-site.

[0088] Preferably, the maintenance suggestions include: The fault location accuracy is not less than 95%; Recommendation of the maintenance time window; Spare parts inventory matching information.

[0089] In some embodiments, through the collaborative work of the multi-source fusion diagnosis mechanism and the physical constraint neural network model, the fault location accuracy is not less than 95%. Specifically, the system uses the three-phase voltage and current waveforms collected by the precision inspection and diagnosis device of the asynchronous motor, combines multi-modal data such as partial discharge, temperature, and vibration, and inputs them into multiple fault branches of the sub-machine learning model. Through feature cross-enhancement and comparison with the fault label library, high-precision fault location identification is achieved. For the recommendation of the maintenance time window, the system combines the equipment operation duration, the current health score trend, and the fault evolution rate curve to predict the fault risk growth interval within the next 3 to 7 days, and generates the optimal shutdown maintenance recommendation time period. The maintenance time window recommendation RUL prediction model: uses a 30-day rolling history and inputs a multi-layer LSTM-ATT to predict the remaining life curve; the production constraint engine: calls the power generation load plan from the MES, uses 0-1 integer programming to solve the minimum power generation loss ΔP, and obtains the set of available shutdown dates; window scoring: calculates f = α·risk reduction rate + β·load tolerance for each date in the set, where α and β are given by the operation and maintenance strategy file; selects the date with the largest f as the "optimal shutdown maintenance window" (i.e., the optimal shutdown maintenance recommendation time period). The spare parts inventory matching information is obtained by linking with the enterprise resource management system (ERP) or the spare parts management database, and the current available inventory, model matching degree, and procurement cycle are retrieved in real time. The automatically compares the currently determined fault component type and outputs the optimal spare parts replacement plan. By comparing the BOM material code of the fault location with the inventory code, if the same model is out of stock, the "substitute part similarity" calculation is performed according to the ISO 15243 / ISO 281 parameters (size ±5%, load ±10%, material consistency); for the maintenance personnel to make a quick decision, so as to realize the intelligent and accurate display of maintenance recommendations.

[0090] The above-mentioned feature cross-enhancement and comparison with the fault label library are achieved through the following methods. First, a multi-modal feature matrix is generated for the original sequences such as three-phase voltage, current, vibration, temperature rise, and PDI, and time-domain statistics, FFT / envelope spectrum, negative sequence impedance, and temperature rise gradient are extracted from the original sequences as 42-dimensional basic features; cross-enhancement: based on the Gradient Boosted Decision Tree, calculate the mutual information of each feature, and select the feature pairs with mutual information > 0.15 to perform Hadamard multiplication or quotient ratio to form 180-dimensional cross features; the fault label vector library: offline collect 1500 samples of inter-turn, broken bar, bearing, etc. that have been manually confirmed, and cluster the enhanced features after UMAP dimensionality reduction to obtain 9 label center vectors; online identification: calculate the cosine similarity between the real-time features and each label center vector. If the cosine similarity ≥ 0.95, the corresponding location is located; combined with the confidence interval, the location accuracy ≥ 95% is output.

[0091] Preferably, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture, including: A physical constraint neural network (PC-DNN), which embeds the motor electromagnetic-mechanical coupling equation as a physical constraint layer in a deep neural network, is implemented in the following ways: In the stator winding fault diagnosis branch, the Maxwell's equations are discretized into differentiable operators to force the network output to satisfy the electromagnetic field distribution law; In the bearing fault diagnosis branch, the stress distribution calculated by the Hertz contact theory is injected as prior knowledge into the initialization of the convolutional kernel weights.

[0092] In some embodiments, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture, which includes a "Physical Constraint Deep Neural Network (PC-DNN)" module for embedding the motor electromagnetic-mechanical coupling equation as a physical prior in the deep neural network to improve the generalization ability of fault diagnosis under small sample and high-complexity working conditions. The model structure and operation process can be described as follows: The overall framework of multi-modal meta-learning, input data: The system collects multi-source information such as current, voltage, vibration, temperature, etc. from the precision inspection and diagnosis device of the asynchronous motor and other sensors, as well as the characteristic indicators given by the fault diagnosis algorithm library (such as negative sequence current component, harmonic amplitude, vibration envelope characteristics, etc.), which together constitute the multi-modal input of the model; Meta-learning enhancement: After initial training, the model can continuously adapt to new working conditions or new device data through incremental learning or adversarial domain adaptation technology (ADA), avoiding repeated training from scratch and ensuring the ability to continuously evolve in complex scenarios. The structure of the Physical Constraint Deep Neural Network (PC-DNN). The main body of the deep network: The PC-DNN consists of several convolutional / fully connected layers, specifically for the high-dimensional multi-source data of asynchronous motor fault detection. Branches corresponding to different fault types (stator winding faults, rotor faults, bearing faults, etc.) are set in the network structure; Physical prior layer: At the key layer of the network, the motor electromagnetic-mechanical coupling equation is introduced into the network loss function or network structure in a differentiable operator manner, so that the network output needs to satisfy or approach the corresponding physical equation constraints when updating the weights. The stator winding fault diagnosis branch: The Maxwell equations are discretized into differentiable operators. Discretization of the Maxwell equations: The electromagnetic field distribution of the motor stator winding is discretized by methods such as finite element or difference to obtain a set of differentiable equations approximately describing the relationship between stator magnetic flux, magnetic flux density and current; Embedding method: During the training process, the PC-DNN will add an additional item "physical constraint loss" to the loss function to measure the difference between the network output (such as the prediction of stator magnetic flux distribution) and the calculation result of the discretized Maxwell equations; Forcing the network output to satisfy the electromagnetic field distribution law: When the network prediction deviates too much from the physical truth, the physical constraint loss increases accordingly, and backpropagation will adjust the weights and prompt the network to be more in line with the real electromagnetic field distribution. In this way, even if the training samples are limited, the diagnosis results of stator winding inter-turn faults can maintain physical rationality and high accuracy.Bearing fault diagnosis branch: Inject the stress distribution calculated by Hertz contact theory as prior knowledge into the initialization of convolutional kernel weights. Hertz contact theory: By calculating the contact pressure distribution, stress peak position, etc. between the rolling element and the bearing raceway, the influence mode on vibration signals or acoustic emission signals can be inferred at the initial stage of bearing fatigue or surface spalling. Initialization of convolutional kernel weights: In the convolutional branch of the PC-DNN responsible for processing bearing vibration or acoustic emission data, its convolutional kernel is initialized with reference to the spatial distribution law of Hertz contact stress during the network initialization or pre-training stage. Enhancement of fault sensitivity: Since the initial weights of the convolutional kernel already contain the characteristic structure of bearing contact stress, when the network faces real vibration data, it is more likely to learn the key signal patterns of spalling cracks or abnormal wear, accelerating the convergence speed and improving the sensitivity to bearing faults. Model training and inference process, Training stage: Use historical fault data and normal operation data to conduct preliminary training on the PC-DNN; during the training process, evaluate the matching degree between the network output and Maxwell's equations through "physical constraint loss", and at the same time retain the advantages of the convolutional kernel initialized from Hertz contact theory; if a multi-modal meta-learning enhancement mechanism is added, it can also be quickly adapted through incremental learning methods when new devices or new fault modes appear. Online inference stage: When the system receives real-time sensor data (current, voltage, temperature, vibration, etc.), each fault branch of the PC-DNN outputs a fault index or a health score respectively; if the stator winding fault branch detects abnormal negative sequence impedance and unreasonable magnetic flux distribution, a risk warning of inter-turn short circuit is issued; if the bearing fault branch identifies abnormal vibration in the high stress area, a bearing wear or spalling fault is determined and maintenance suggestions are pushed. Adaptability to small sample scenarios: When it is difficult to obtain a large amount of complete fault data on site, the "network + physical equation" can force the model to comply with the real mechanism and reduce the risk of overfitting driven by pure data; Diagnostic accuracy and interpretability: The output of the stator winding branch can be verified at the magnetic flux distribution level, and the bearing fault branch can be traced back to the stress distribution theory, making it easy for maintenance personnel to understand the model basis; Accelerated convergence: Through the physical prior initialization method of the convolutional kernel, not only the training cycle is shortened, but also the network learns a more targeted feature extraction mode. It can be seen that adopting a multi-modal meta-learning enhancement architecture and embedding electromagnetic-mechanical coupling equations such as Maxwell's equations and Hertz contact theory in the deep network can not only improve the diagnostic accuracy in a small sample environment, but also ensure that the determination results of the model for motor faults are more in line with the physical mechanism, comprehensively improving the credibility and sensitivity of fault diagnosis.

[0093] In some embodiments, the sub-machine learning model adopts a multi-modal meta-learning enhancement architecture, and further includes: Dynamic graph attention support vector machine (DGAT-SVM), which constructs a dynamic graph structure based on the device operation topology relationship, where: The node features include real-time current harmonic distortion rate, vibration spectrum entropy value, and temperature gradient; The edge weights are dynamically updated according to the electrical connection strength between devices and the historical fault propagation probability; The attention mechanism is used to focus on the key fault correlation paths, and the device-level and system-level risk scores are output.

[0094] In some embodiments, such as Figure 2 As shown, large-scale health monitoring and remote diagnosis are realized based on a four-level hierarchical architecture of "field - enterprise - industry - country": The field layer includes various on-site devices (such as main motors, fans, pumps, etc.). The original data such as current, voltage, vibration, partial discharge, and temperature are sent into the enterprise local area network through local acquisition terminals. The terminals usually access the switch through Gigabit Ethernet or LoRa / 5G and communicate with the backend in seconds. The enterprise local area network layer includes a data server responsible for the management of real-time databases and historical databases, saving all monitoring data; an enterprise communication proxy server responsible for protocol conversion, data encryption, and external publication; an enterprise diagnosis and early warning workstation deploys a parent CNN and a sub-machine learning model to comprehensively score the on-site data and classify faults; mobile and fixed users (mobile phones, tablets, duty PCs) access the workstation through the intranet or VPN client to view digital twins, alarm records, and maintenance suggestions. The boundary security and industry network access include three security devices, namely a gateway (i.e., the gateway in the figure), a router, and a firewall, configured at the enterprise exit: The gateway completes address conversion, the router executes QoS and dynamic routing, and the firewall implements white lists, intrusion detection, and port isolation; after security auditing, the selected health data, alarm summaries, and energy efficiency indicators are admitted to the industry local area network. The industry local area network layer includes an industry diagnosis and early warning platform. This industry diagnosis and early warning platform aggregates the data uploaded by multiple enterprises and uses big data models for horizontal comparison and group trend prediction; on-duty experts can view the risk rankings, spare part shortages, and energy efficiency of each member unit through desktop terminals or mobile terminals. At the same time, the platform generates industry-level handling suggestions for severe alarms and returns them to each enterprise. The national diagnosis and early warning center includes that the industry platform periodically pushes summary information such as high-risk device lists and abnormal carbon emission trends to the national center through a VPN dedicated line; the national diagnosis and early warning center uses a machine learning model with a larger sample for macro risk judgment and policy-level guidance, and then issues scheduling instructions or early warning notices to the industry platform through a dedicated line to achieve a vertical closed loop. The data flow and security control include three-stage encryption of "end - pipe - cloud" for the entire link: TLS + AES is used from the field to the enterprise intranet; IPsec is used from the enterprise to the industry network; VPN tunnels and national cryptography algorithms are used from the industry network to the national center. Multi-level identity authentication ensures the integrity, traceability, and anti-tampering of data during cross-domain transmission. Through Figure 2 As shown in the topology, the present invention realizes hierarchical data aggregation, intelligent analysis, and remote collaborative disposal from on-site devices to the national-level platform, which not only meets the rapid early warning within the enterprise but also takes into account the horizontal comparison of the industry and the national macro supervision, with a clear system structure and complete logic.

[0095] In some embodiments, various types of on-site device information are collected, and data transmission is achieved through enterprise local area networks and industry local area networks; the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture. In order to conduct more refined linkage fault analysis on the interconnected system formed by multiple devices in the factory (such as generators, asynchronous motors, transformers, etc.), a "Dynamic Graph Attention Support Vector Machine (DGAT-SVM)" module is introduced. Its operation mode and process are roughly as follows: Construction of the dynamic graph structure, Device nodes: Key devices in the power plant (such as asynchronous motors, transformers, generators, etc.) are regarded as nodes, and each node has several dynamic characteristics, including current harmonic distortion rate, vibration spectrum entropy value, temperature gradient, etc. Definition of edges: If there is an obvious coupling between two devices electrically or mechanically (such as a motor and a transformer under the same busbar segment, mechanical coupling drive, etc.), an edge is added to the graph; initially, the weight of the edge can be set according to factors such as the electrical connection strength or physical distance between the devices. Dynamic update of node features and edge weights, Node features: During actual operation, the current harmonic distortion rate, vibration spectrum entropy value, and temperature gradient of each device will continuously evolve due to load changes, ambient temperature fluctuations, or fault symptoms. DGAT-SVM periodically captures these feature data and updates the attributes of each node in the graph. Adaptive edge weight: If historical data indicates that a certain motor often causes abnormalities in another device sharing the same busbar after a failure (high probability of fault propagation), the edge weight will increase; otherwise, it will decrease. This can reflect the "active edges" and "low-risk edges" of fault propagation at the system level. Attention mechanism focuses on key fault correlation paths, Attention scoring: When DGAT-SVM traverses the dynamic graph, it calculates the risk degree of each node (device) and the fault correlation degree between it and its neighbor nodes. The attention mechanism scores the connections between devices based on edge weights, node feature similarity, and known fault history records, highlighting the paths most likely to form a "fault chain" or linkage risk. Local subgraph mining: The attention mechanism focuses on in-depth analysis on high-scoring edges or nodes, which helps to quickly locate the most potential nodes and coupling loops in scenarios where the number of devices in the whole plant is large and the fault relationships are complex, reducing the interference of irrelevant information. Support Vector Machine (SVM) score output, Device-level risk: On the focused subgraph or important nodes, DGAT-SVM inputs node features (such as real-time current harmonic distortion rate, vibration spectrum feature value, temperature gradient, etc.) into the SVM classification / regression model to output the risk score or health score of each node. For suspected fault nodes, SVM can give classification results (such as "bearing fault", "stator winding fault", etc.) or fault probabilities. System-level risk: If the attention mechanism detects a strong coupling fault chain formed between multiple device nodes, DGAT-SVM will calculate the system-level risk score, indicating the probability or severity of the potential linkage fault that the current entire plant area or unit may face.This score can be used by the dispatching center or management to take intervention measures in a timely manner (such as adjusting the operation mode, pre-enabling standby units, etc.). Fault warning and grading: After the DGAT-SVM obtains the equipment-level and system-level risk scores, if the score exceeds the threshold or increases significantly, the warning module is triggered to mark the equipment nodes that most need attention or maintenance and the possible conduction paths for the operation and maintenance personnel; Historical database and model iteration: The system compares the output score with the actual maintenance results or subsequent fault events, and continuously corrects the attention scoring and SVM classifier parameters to make the model more accurate in identifying the future fault correlation propagation. Through the above process, this embodiment effectively identifies the risk of linkage faults between devices by using the dynamic graph attention support vector machine (DGAT-SVM) under the multi-modal meta-learning enhanced architecture: It can not only give the fault determination of a single device, but also evaluate the overall fault propagation trend of the system, helping the operation and maintenance and dispatching departments better control the health status of power generation equipment, thereby reducing the incidence of linkage faults and improving the reliability of safe operation.

[0096] In some embodiments, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture and further includes: A cross-domain incremental learning module, including: A simulation-measured data alignment unit that uses the adversarial domain adaptation ADA technology to eliminate the feature distribution deviation between the simulation model and the real device; An online knowledge distillation pipeline that encodes the expert diagnosis rules in the historical fault case library into a lightweight decision tree and outputs for consistency constraint.

[0097] In some embodiments, the sub-machine learning model further incorporates a cross-domain incremental learning module under a multi-modal meta-learning enhancement architecture, aiming to address the challenges of "the differences between the simulation environment and the actual measurement field environment" and "the continuous emergence of new fault modes or diagnostic knowledge during on-site operation". This module consists of two key units: the simulation-measurement data alignment unit and the online knowledge distillation pipeline, and the operation process is as follows. For the simulation-measurement data alignment unit, in the power generation environment, fault data is scarce and diverse in distribution, and it may not be possible to collect sufficient actual measurement samples for each fault mode. Simulation platforms (such as finite element simulation, motor fault simulation) can generate a large amount of virtual fault data, but the data distribution often differs significantly from that of real devices, such as noise level, environmental disturbance, load randomness, etc. The adversarial domain adaptation (ADA) technology applies the domain adaptation principle: through an adversarial neural network or an autoencoder, the feature distributions of the "simulation domain" and the "measurement domain" are made to converge in the high-dimensional space, reducing the problem of the model being over-fitted to the simulation data. The specific process is as follows: The simulation data and a small amount of actual measurement data are simultaneously input into a domain discriminator. The main network (generator / encoder) continuously learns how to map the simulation features to a representation space that is closer to the actual measurement feature distribution. The domain discriminator then attempts to distinguish whether the input is from simulation or actual measurement. When the discriminator has difficulty differentiating, it indicates that the feature distribution alignment effect is good. The benefit of the model: After completing the domain alignment, the simulation samples can produce more consistent effects with the actual measurement environment during the training of the sub-machine learning model. Even if the actual collected fault data is limited, the simulation data can still be fully utilized to improve the model's robustness.

[0098] For the online knowledge distillation pipeline, the historical fault case library and expert diagnostic rules. Power plants often accumulate years of operation and maintenance experience and fault cases, which contain "diagnostic rules" or "decision trees" summarized by experts. For example: If the negative sequence current of the motor stator exceeds a certain value and the temperature rise slope exceeds a certain threshold, it is judged that there may be a turn-to-turn fault. If the vibration envelope spectrum continuously increases in a specific frequency band, it indicates the risk of bearing spalling. These "manual rules" can quickly locate common faults but lack flexibility for new fault modes. The lightweight decision tree encoding and distillation process: Lightweight decision tree: Simplify the numerous expert rules into one or more small decision trees, perform logical branching on the main measurement points and thresholds, and compress the rule set with fewer nodes. Online knowledge distillation: While the sub-machine learning model (such as PC-DNN, DGAT-SVM) outputs the fault risk, the distillation pipeline will also obtain the judgment results of the lightweight decision tree. If there is a large deviation from the model inference result, the model weights are corrected through a consistency constraint term during the training or incremental learning process to make it as consistent as possible with the expert rules for common fault types. Advantage: The distilled model can not only retain the fast and stable judgment of the expert rules for mature fault scenarios but also capture new patterns and complex signal features that are not explicitly covered by the expert rules through deep learning.

[0099] Data acquisition and preprocessing. The multi-source data access module obtains real-time monitoring values such as voltage, current, vibration, and temperature, while the simulation platform provides virtual fault data. The simulation data is mapped to a feature space closer to the measured domain under adversarial domain adaptation, and then jointly sent to the sub-model with the measured samples for training or incremental learning. Online incremental update. When new fault modes, equipment modifications, or operating condition switches occur on-site, the original model may not be able to accurately judge. The cross-domain incremental learning module will automatically call the simulation data for re-alignment and use a small number of samples collected on-site for rapid fine-tuning. The online knowledge distillation pipeline also compares with the existing expert decision tree. If the model output deviates from the mature rule branch, consistency correction is performed. Fault determination and self-learning. The generated or updated model outputs the fault risk levels for each motor or device. The operation and maintenance personnel check the model results. If a fault is successfully identified or the determination fails, the new label information can be sent back to the database and the model is continuously iteratively adjusted. Simulation and measurement collaboration. By using the adversarial domain adaptation (ADA) technology, the problem of simulation-measurement distribution difference is solved, significantly improving the diagnostic reliability and generalization ability of the model in the real production environment and reducing the over-reliance on the long-term accumulated fault data on-site. Expert experience integration. The online knowledge distillation pipeline integrates the valuable artificial decision tree into the sub-model, which not only speeds up the discrimination speed of common faults but also ensures that when new fault forms appear, the deep learning model can still be continuously updated and not be restricted by traditional rules. Real-time incremental learning. The model can continuously perform small-scale fine-tuning when the equipment operating conditions change, gradually adapting to the data characteristics of the new load environment, temperature range, or after structural modification, and always maintaining a relatively high diagnostic accuracy. For small samples, complex on-site conditions, or fault scenarios that are difficult to reproduce, the cross-domain incremental learning module not only significantly improves the adaptation speed of the model but also ensures that the existing expert knowledge is not erased, thus achieving the optimal effect of "human-machine collaboration" and "simulation-measurement integration".

[0100] Preferably, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture, and further includes: An uncertainty quantification engine, which evaluates the credibility of the diagnostic result in the following ways: Applying Monte Carlo Dropout perturbation to the output of PC-DNN to calculate the confidence interval of the fault probability distribution; Introducing a fuzzy membership function into DGAT-SVM to quantify the progressive process of the equipment state deviating from the normal threshold.

[0101] In some embodiments, the training data of the sub-machine learning model further includes: Multi-physical field coupling fault dataset based on finite element simulation, covering compound fault scenarios such as rotor eccentricity and insulation aging; the training data of the sub-machine learning model is partially from the multi-physical field coupling fault dataset generated by the simulation platform, which is obtained by constructing a three-dimensional electromagnetic-thermal-stress field model through a motor finite element modeling tool (such as ANSYS Maxwell, etc.). Taking the compound scenario of rotor eccentricity and insulation aging as an example, the system first establishes the structural geometry model of the asynchronous motor, sets the working condition parameters such as the stator and rotor material parameters and air gap asymmetry, realizes the dynamic eccentricity simulation by changing the rotor position, and then superimposes the thermal aging factors such as the decrease in the thermal conductivity of the insulation layer and the increase in thermal aging time, jointly solves the electromagnetic field and temperature field distributions, extracts the corresponding time series data (such as stator leakage magnetic density distribution, insulation hot spot temperature, etc.), and finally forms a labeled fault sample set. This type of data can cover abnormal scenarios that are difficult to reproduce or collect under working conditions, significantly enhancing the generalization ability of the model. In addition, the full life cycle carbon footprint data of the equipment is collected in collaboration with the system operation management platform. Specifically, during the equipment operation stage, the system continuously records its power output, power quality, motor efficiency change curve, etc., and analyzes the trend of operation efficiency degradation in combination with on-site environmental parameters (such as temperature, humidity, load conditions). At the same time, the carbon emission value corresponding to the unit energy consumption during the operation period (kgCO 2 / kWh) is accumulated through the carbon measurement module, and then combined with the carbon emission estimation data in the stages of equipment manufacturing, transportation, maintenance, disassembly, etc. to form a carbon footprint file for the entire life cycle. In model training, this carbon footprint data, together with historical fault records and health score trends, is input into the sub-model to learn the internal correlation between energy efficiency degradation and fault modes, and to improve the model's capabilities in aspects such as energy-saving state prediction and green operation and maintenance recommendations. Therefore, the training data acquisition of this system comes from the fusion of highly reliable simulation and full-process measured data, which not only solves the problem of generating fault samples in the few-sample scenario, but also expands the intelligent model's understanding ability of the relationship between equipment performance - energy efficiency - carbon emission, providing data guarantee for the intelligent operation and maintenance and green operation and maintenance of power generation equipment. The full life cycle carbon footprint data of the equipment is used to correlate the relationship between fault modes and energy efficiency degradation.

[0102] In some embodiments, the sub-machine learning model adopts a multi-modal meta-learning enhanced architecture, and to improve the interpretability and reliability of the device fault diagnosis results, an "uncertainty quantification engine" module is additionally set up. This module comprehensively evaluates the credibility and progressive deviation degree of the diagnosis results by introducing Monte Carlo Dropout and fuzzy membership functions into the physics-constrained neural network (PC-DNN) and the dynamic graph attention support vector machine (DGAT-SVM) respectively. The specific implementation process is as follows: Application of Monte Carlo Dropout in PC-DNN. PC-DNN (physics-constrained neural network) usually turns off Dropout by default during the inference phase (which only plays a role in suppressing overfitting during training), while "Monte Carlo Dropout" retains the mechanism of randomly setting zeros in Dropout even during the inference phase, and statistically analyzes the output distribution after multiple forward calculations. When the same batch of data is input, each forward propagation will have slight differences due to the randomness of Dropout, so a set of fault probability or health score samples can be obtained, and their mean and standard deviation can be estimated.

[0103] Add a Dropout layer after the key convolutional or fully connected layer of PC-DNN, and keep it randomly masking the activation units during the inference phase; for a period of time window or a single sampling data, perform multiple (such as 20 or 50 times) forward inferences, and record the fault probability or score each time; perform statistical analysis on all inference results to obtain the mean (as the final diagnosis value) and the standard deviation or interval range (as the uncertainty measure). Output the confidence interval. If the standard deviation is small, it indicates that PC-DNN has a high confidence in the fault determination; if the standard deviation or interval is large, it indicates that the model may have uncertainties for the current working condition and further manual investigation or more data support is needed. In the visualization interface, the fault score can be presented in the form of an interval strip, allowing the operation and maintenance personnel to quickly judge the "degree of confidence" of the model output. Introduction of the fuzzy membership function in DGAT-SVM. Measure of node state deviation. DGAT-SVM constructs a dynamic graph structure between devices, and node features such as current harmonic distortion rate, temperature gradient, vibration energy, etc. will fluctuate at any time; to reflect that there may be a "gradual transition" rather than a "one-size-fits-all" boundary between the device state from "normal" to "abnormal", this embodiment introduces a fuzzy membership function during SVM discrimination.

[0104] Fuzzy membership function implementation, for the classification hyperplane or regression boundary of SVM, set the fuzzy membership function μ( ), with a value range of 0, 1; when the device node is far away from the discrimination boundary and is in the normal range, μ≈0 means the deviation is very small; when the device node feature approaches or exceeds the boundary, μ gradually rises to 1, indicating that the fault is approaching asymptotically. Quantification of the asymptotic process, focusing on the nodes and edges with the most signs of faults through the attention mechanism, and calculating their fuzzy membership scores; if the μ value of a node continues to hover at a medium-high level, it means that the equipment has shown obvious abnormal trends but has not completely failed, and the operation and maintenance personnel can perform advance maintenance or status tracking based on this. The "system-level risk score" output by the SVM can also be included in the fuzzy results. If multiple nodes are at a medium deviation, the system level may face the risk of linked failures. Overall uncertainty assessment process, data input: such as Figure 3 As shown in the figure, the electrical side signal is obtained by extracting the three-phase current and voltage from the secondary side of the control cabinet CT / PT, and coupling through a dedicated current-voltage sensor to ensure the safety isolation of the primary busbar; mechanical side signal: an acceleration vibration sensor (optional acoustic emission probe) is arranged on the motor bearing or base to capture the characteristics of mechanical faults such as bearing peeling and rotor eccentricity. The acquisition module of the signal acquisition system contains a multi-channel synchronous A / D and isolation conditioning circuit, which synchronously samples the current, voltage waveform and vibration signal at a set frequency (1-5 kHz) and timestamps each frame of data. The electrical quantity is first converted into amplitude / phase quantity; the vibration quantity is digitally filtered after analog pre-amplification, and uniformly packaged into a feature package and sent to the host computer (including monitoring, diagnosis and early warning software). The monitoring, diagnosis and early warning software runs on the embedded industrial computer or edge computing node in the control cabinet, obtains a comprehensive health score through the parent convolutional neural network model, and then calls the child machine learning model to make branch judgments on stator winding turn-to-turn, rotor bar breakage and bearing faults. When the risk index is lower than the threshold, the system only archives it; when it reaches the warning interval, a maintenance time window recommendation is generated; when it enters the alarm zone, an audible and visual alarm is triggered and immediate maintenance is recommended. All results are displayed in real time on the LCD screen on the cabinet, which is convenient for on-site inspections. The diagnostic software of the background management system sends the health score, fault type, and vibration-electrical characteristics to the data center via Ethernet or LoRa / 5G gateway. The background management system centrally archives the historical curves, spare parts inventory, and maintenance records of different units, and provides APIs to the enterprise or industry cloud platform. If similar faults occur in multiple motors, the background can automatically generate a group analysis report to assist in operation and maintenance decision-making. The power supply and safety devices are powered by the control cabinet, and a UPS module is installed inside to prevent instantaneous power failure; all sensor channels meet the 2.5 kV isolation and IEC 61000-4-4 anti-interference requirements to ensure data and personnel safety in high-voltage environments. Through Figure 3In the structure shown, the present invention completes signal acquisition, edge diagnosis, and cloud management within a closed loop: two information flows of current / voltage and mechanical vibration converge at the acquisition layer, are fused and analyzed through the algorithm layer, and then are sent to the management layer for closed-loop operation and maintenance. This solution not only avoids large-scale modifications to the original control loop but also realizes high-precision online monitoring of multiple-source early faults of the motor.

[0105] In some embodiments, the system also collects multiple signals, and multi-source data (such as voltage, current, vibration, temperature, etc.) are inferred in parallel or serially by PC-DNN and DGAT-SVM; the PC-DNN outputs a confidence interval: multiple forward calculations are performed by executing Monte Carlo Dropout to obtain the average diagnosis value and variance, which are used to construct a fault probability or health score interval; the DGAT-SVM performs fuzzy discrimination: the node (device) features are input into the SVM for classification or regression, and at the same time, the fuzzy membership function is calculated to output the progressive deviation degree; comprehensive credibility: if the PC-DN interval is too large or the DGAT-SVM fuzzy value continues to increase, the uncertainty quantification engine gives a "warning" and recommends further manual inspection or model retraining; otherwise, the diagnosis result is relatively stable and credible. Improve the interpretability of fault diagnosis: Through the confidence interval and fuzzy membership degree, the operation and maintenance personnel not only know whether the model determines "whether there is a fault" but also can evaluate the reliability of the determination and the progressive amplitude of the fault risk; reduce the risk of misjudgment or missed judgment: In scenarios where the load fluctuates frequently or the measurement noise is large, if the uncertainty measure is high, the system can automatically relax the fault alarm threshold or require manual review; assist in operation and maintenance decision-making: For situations near the critical value, if the fuzzy membership degree indicates "continuous deviation intensification", preventive maintenance should be arranged in a timely manner; if the interval fluctuation is small and the μ value is extremely low, it can continue to operate safely, improving the equipment availability.

[0106] In some embodiments, the wireless communication module includes a heterogeneous communication gateway that supports dual-mode communication between LoRa low-power wide-area network and 5G network, and adopts a dynamic routing algorithm based on the device topology structure. The wireless communication module adopts a heterogeneous communication gateway architecture, integrates LoRa and 5G communication modules, and realizes dual-mode parallel communication and intelligent switching through a unified network management interface. Specifically, the gateway is equipped with an independent LoRa communication chip (such as the Semtech SX127x series) and a 5G communication module at the hardware level, which are scheduled and controlled by a central microprocessor. The software layer loads dual-channel drivers through an embedded operating system (such as OpenWRT or embedded Linux) and runs protocol adaptation middleware, enabling it to access the LoRa private gateway and the public network 5G core network respectively, realizing dual communication for one device, resource redundancy, and intelligent network selection. In terms of communication strategy, the system automatically selects the preferred link based on a network quality evaluation mechanism (including indicators such as link delay, packet loss rate, RSSI, etc.). For example, in the underground area of a power plant or a high electromagnetic interference area, LoRa with strong anti-interference ability, wide coverage, and low power consumption is preferentially used for transmission; while in data scenarios that require high frequency, high bandwidth, and low-latency uploads (such as partial discharge high-frequency waveforms or AI model results), the 5G channel is switched to ensure real-time performance and transmission reliability. In addition, it supports a dual-link automatic fault switching and data retransmission mechanism to ensure that monitoring is not interrupted when single-link communication is abnormal. Regarding the dynamic routing algorithm based on the device topology structure, the present invention constructs a device network topology map based on the physical connection relationship and the logical adjacency relationship of the communication layer between each field device, and adopts an improved dynamic AODV protocol as the underlying routing mechanism. The gateway regularly broadcasts device status, link quality, and data transmission pressure information, and dynamically generates the optimal path through weight calculation. The weight factor includes physical distance, communication success rate, device level (such as whether it is a core node), historical failure frequency, etc. In the scenario of a large number of sensor nodes deployed, this algorithm can automatically form a multi-hop routing path with fault tolerance, and dynamically update the routing table when a device is newly added or taken offline, maintaining network connectivity and efficiency.

[0107] Preferably, the activation function adopted by the mother convolutional neural network model is expressed as follows: ; The activation function adopted by the sub-machine learning model physical constraint neural network is expressed as follows: ; Wherein, represents the input value of the current neuron, e is the base of the natural logarithm; s represents the slip rate of the asynchronous motor, f is the fundamental frequency of the power supply, represents the electrically-side frequency offset monitored in real time, that is, the difference from the nominal frequency of 50 Hz; is the dynamic change quantity on the environment side, that is, the sum of the normalized temperature, wind speed, and load changes; is the coupling coefficient. The larger the value, the more sensitive it is to the changes in the dynamic characteristics of the motor. The activation function of the mother convolutional neural network adds an adjustment amount of "slip ratio" on the basis of the conventional activation function, so that the activation curve shifts according to the motor slip value in the exponential part. When the load increases and the slip ratio rises, the activation function automatically translates, making the sensitive interval of the network for the input change accordingly, so as to capture the stator winding fault symptoms more accurately. The slip ratio is an important operating parameter of the asynchronous motor. The network directly combines the slip ratio at the activation level, which can reduce the dependence on a large amount of training data and better adapt to the rapidly changing working conditions. The activation function of the physical constraint neural network (improved Tanh) of the sub-machine learning model adds a control factor at the input end of the traditional hyperbolic tangent function, and its value depends on the sum of the "electrical side frequency offset" and the "environment side dynamic quantity". And a coupling coefficient is set to adjust the influence intensity of external disturbances on the activation value. By comparing the power supply reference frequency with the real-time monitored frequency offset and superimposing it with environmental variables such as temperature, wind speed, and load disturbances, the network can fully consider various external factors when activating the output. For example, when the power supply offset amplitude increases and the environmental temperature rises sharply, the activation function will enter the saturation value faster in the positive direction, helping the model quickly identify potential faults related to this.

[0108] The mother convolutional neural network (mother CNN) is used for non-linear transformation after the convolutional layer: After each convolutional operation or batch normalization operation, the activation unit performs a non-linear mapping based on the convolutional output and the slip rate value. Before connecting to the fully connected layer or pooling layer: Usually in the middle and late stages of the network, the activation function follows the convolutional layer or pooling layer at the backend, and is used to enhance the recognition of features such as stator current and negative sequence impedance. By combining the slip rate information, the network can more effectively distinguish the signal fluctuations caused by load changes from the real fault features. The sub-machine learning model (physically constrained neural network), in the fault branch or fusion layer: Since this model extracts features for different branches such as the motor stator winding, bearing, and rotor respectively, the activation function is deployed at the key nodes after the convolutional / fully connected layer of each branch to couple the electrical side frequency offset and environmental data into the network output in real time. When integrating the sub-network outputs: Before combining the judgment results of multiple branches to form a comprehensive health score, the activation function can be applied again to ensure that the final output has sufficient response ability to external disturbances under the current working conditions. Dynamically adjust the sensitive interval of neurons. When the motor load or environmental disturbance is relatively stable, the activation function only makes a small offset; while in the case of frequent start-stop or high-load working conditions, the activation curve will change significantly, enabling the network to maintain a high-resolution recognition of potential fault features. Through the adaptive difference of the slip rate and environmental variables, false negatives or false positives in extreme working conditions can be reduced. Echoing with the physical equations, in the sub-network, after combining the motor electromagnetic-mechanical coupling equations, the activation function synchronously uses "power frequency offset + environmental variables" to achieve a more realistic output change, avoiding prediction results that violate the real mechanism. This mode also reduces the network's sole reliance on large-scale training samples, enabling it to maintain a better diagnostic effect even with small samples or continuously changing working conditions. Improve the speed of early fault recognition. Since the activation curve has an adjustable gain for various external disturbances, if the motor shows abnormal features (such as a small negative sequence impedance jump or frequency drift) during the initial stage of a fault, the network will detect the signal abnormality faster and accelerate the warning feedback. Therefore, in this embodiment, by introducing the slip rate and electrical / environmental coupling quantities into the activation functions of the mother convolutional neural network and the sub-machine learning model respectively, the network can still accurately track the fault features and provide reliable health assessment results when dealing with complex scenarios such as high load changes, frequency offsets, or temperature shocks.

[0109] The mother convolutional neural network model (mother CNN) is used to perform large-scale deep convolutional operations on massive sensing data (such as voltage, current, vibration, temperature, etc.), focusing on extracting multi-modal global features; it is inclined to perform multi-level fusion on the overall fault features under complex working conditions to generate a comprehensive health score or a global fault index. By using several convolutional layers, pooling layers and fully connected layers, it extracts high-level semantic features (such as the negative sequence impedance trend of the motor, harmonic concentration, abnormal temperature distribution) and finally outputs one or more judgment scores; in some embodiments, certain intermediate layer outputs (deep feature embedding vectors) of the mother CNN can also be used as shared features for other algorithms. The finally produced health score is usually a floating value between 0 and 100 or 0 and 1, which is used to measure the current health status of the device; or it outputs a comprehensive fault index (such as "fault probability 0.8", "abnormality 0.6"), which is suitable for quickly presenting the overall condition of the device in a visualization interface.

[0110] The sub-machine learning model (sub-model) conducts more targeted and physically constrained refined analysis on specific fault modes (such as inter-turn short circuit of stator winding, broken rotor bars, bearing spalling, etc.); it integrates mechanisms such as physical constraint neural network (PC-DNN), dynamic graph attention support vector machine (DGAT-SVM) or incremental learning module to conduct in-depth determination of specific working conditions or coupled faults. When targeting a certain type of fault, it will introduce professional prior knowledge such as motor electromagnetic-mechanical coupling equations, Hertz contact stress distribution or fault propagation topology maps, so that the network or algorithm has higher sensitivity to that type of fault mode; after processing the fault features, the sub-model will output detailed fault risk levels, evolution trends or uncertainty quantification intervals, etc., which can be used for hierarchical alarm or maintenance suggestions.

[0111] In some embodiments, each fault mode corresponds to a score or level (such as "Inter-turn fault risk: high", "Probability of rotor bar breakage: 30%", "Bearing health score: 78 / 100"); if integrated incremental learning is used, the model may also output the confidence interval of the difference in the current data distribution, facilitating managers to understand the reliability of the diagnostic results. The relationship between the two and the data flow, either in parallel or in series. In some embodiments, the mother CNN and the sub-model can be placed on the same server or deployed on different processing nodes respectively. Their data flows are parallel and independent of each other, without mutual exclusion conflicts. The mother CNN mainly performs global health analysis, and the sub-model focuses on in-depth and detailed faults; the mother convolutional neural network model (mother CNN) and the sub-machine learning model can be flexibly configured as a parallel mode or a sequential call mode in the system deployment structure. In the parallel mode, the two are respectively run by independent threads or processes on different computing cores or container environments of the background server. Specifically, the mother CNN mainly extracts features and performs comprehensive health scoring on large-scale raw data (such as continuous voltage and current waveforms). Its input data stream is connected to the data acquisition system through a cache or shared memory channel, and after processing, the results are uploaded to the health assessment database. The sub-machine learning model, on the other hand, uses structured feature data for fine-grained identification for specific fault types (such as bearings or inter-turn short circuits), usually obtaining the cleaned and normalized fault feature sets, such as negative sequence impedance, harmonic ratio, temperature rise gradient, etc., from the historical feature database or the preprocessing module. The input sources of the two are different and the processing processes are independent. In the sequential call mode, according to the operation strategy, the system can first complete the comprehensive scoring by the mother CNN. If an abnormal trend is found or the score exceeds the preset threshold, the sub-model is called to perform a secondary determination. At this time, the output of the mother CNN is used as a trigger condition, and the sub-model uses finer-grained data for targeted identification and hierarchical analysis. This structure realizes logical sequential arrangement through a task scheduling middleware or a model call engine, avoiding simultaneous competition for data resources. To avoid data conflicts, the model input buffer, output data structure, and scheduling priority are clearly defined in the system design. Therefore, whether deployed on a multi-core server, a Docker container, or an embedded edge computing node, the mother CNN and the sub-model can achieve structural decoupling, resource isolation, and scheduling independence, ensuring the efficiency of the diagnostic process and the stability of the system operation. In some embodiments, the output result of the mother CNN (such as the comprehensive health score) and the fault risk level of the sub-model can also be jointly decided or weighted and fused in the background; if the mother CNN detects an obvious abnormality, the sub-model is called for further investigation, and the hierarchical diagnostic process can also be achieved. The final results are all sent to the warning module and the visual human-computer interaction interface, and the "comprehensive health degree" and "fault risk type / level" can be displayed on the device overview view respectively, for the operation and maintenance personnel to decide on the specific maintenance strategy and timing.

[0112] The present invention provides an integrated power generation equipment health status supervision system, and the beneficial technical effects that can be achieved are as follows: 1. The present invention organically integrates the battery online monitoring, slip ring intelligent data acquisition, dissolved gas analysis in transformer oil, partial discharge monitoring, wireless temperature measurement, and asynchronous motor precision inspection and diagnosis devices that are traditionally deployed dispersedly. Through a unified interface protocol and data management module, it realizes the centralized acquisition and visualization management of multi-source heterogeneous data, greatly reducing the workload of maintenance personnel repeatedly switching between different systems and avoiding the problem of information islands, laying a good foundation for subsequent system expansion and integration. By appending the characteristic data calculated by the fault diagnosis algorithm library (such as negative sequence current component, spectral characteristics, etc.) to the input layer of the mother convolutional neural network model, the present invention can directly integrate the "prior knowledge" of various classical diagnostic methods on the basis of deep learning to extract the characteristics of the original data. This not only further enriches the input dimension of the model but also ensures the complementary advantages of the deep network and traditional diagnostic features, doubling the reliability and sensitivity of fault detection and enabling more accurate and timely identification of early fault signs.

[0113] 2. Through the fault diagnosis and evaluation module, relying on advanced algorithms such as the fusion of the mother convolutional neural network and the physical constraint sub-network, the present invention can quickly identify the early signs of faults such as inter-turn faults in the stator winding of the motor, rotor bar breakage faults, and bearing faults under complex working conditions, and significantly improve the sensitivity and accuracy of fault identification by combining technologies such as negative sequence apparent impedance, stator current modulus spectrum, and multi-modal meta-learning. Once the health assessment level is monitored to be lower than the threshold, the system can automatically trigger an alarm and push maintenance suggestions to avoid unplanned shutdowns caused by the further deterioration of the fault. The present invention predicts the faults of the power generation equipment through the mother convolutional neural network model and the sub-machine learning model to achieve early fault discrimination, greatly improving the accuracy and efficiency of equipment fault identification. The present invention reasonably introduces physical or working condition information such as the motor slip rate and environmental interference coefficient into the activation function of the mother convolutional neural network model, so that the input of the activation function is no longer just the result of a simple linear transformation. The network can adaptively adjust the output sensitivity when facing changing operating conditions. This improved activation function can not only enhance the robustness to complex factors such as electrical side imbalance and load fluctuations but also effectively prevent gradient disappearance or gradient explosion, thereby shortening the convergence time while maintaining high-precision diagnosis.

[0114] 3. In the present invention, a physics-informed neural network (PC-DNN) is added to the sub-machine learning model, and the electromagnetic-mechanical coupling equation of the motor is embedded into the deep network. During the training process, the network not only relies on data-driven methods but also uses physical equations to impose constraints on the network output, forcing it to satisfy the internal physical laws of the motor. This can significantly reduce the dependence on extremely large-scale labeled data, improve the prediction accuracy and stability in complex working conditions or scenarios with insufficient samples, and effectively prevent the network output from producing results that contradict the true physical mechanism, greatly enhancing the reliability and interpretability of fault diagnosis. Introducing a dynamic graph attention support vector machine into the multi-modal meta-learning architecture can, on the one hand, construct a dynamic graph structure model based on the topological relationship of device operation and dynamically update the weights of nodes and edges; on the other hand, through the attention mechanism, it can focus on key fault correlation paths and achieve accurate identification of the global fault propagation link. This combination significantly improves the ability to evaluate the system-level fault risk, can quickly extract key features in situations where fault correlations are complex among multiple devices, reduce interference from irrelevant information, and thus significantly enhance the fault prediction efficiency and real-time response ability of the overall system.

[0115] The above has introduced in detail an integrated power generation equipment health status monitoring system. Specific examples are used in this article to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the ideas and methods of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An integrated power generation equipment health status supervision system, characterized in that, it includes: A multi-source data access module, which is used to access the precision inspection and diagnosis device for asynchronous motors, the TLI-X8 battery online monitoring system, the slip ring intelligent data acquisition system, the on-line monitoring system for dissolved gases and trace water in transformer oil, the on-line partial discharge monitoring device for generators, and the wireless temperature measurement system, and obtain the operation parameters and environmental parameter data of the power generation equipment; A wireless communication module, which transmits the collected operation parameters and environmental parameter data to the data storage module; A data storage module, which is used to store the operation parameters and environmental parameter data from the multi-source data access module; A fault diagnosis and evaluation module, which is used to globally train and calculate the operation parameters and environmental parameter data of the obtained power generation equipment using a mother convolutional neural network model to output a health evaluation level, and use a sub-machine learning model to calculate and judge specific fault modes to output a fault risk level; An early warning module, which is used to generate an alarm signal when the health evaluation level or the fault risk level is lower than the corresponding set threshold respectively; A visual human-computer interaction module, which is used to display the operation status, historical records, alarm information and health evaluation level of the power generation equipment on the platform interface or mobile terminal.

2. An integrated power generation equipment health status supervision system as described in claim 1, characterized in that, The precision inspection and diagnosis device for asynchronous motors includes: An in-situ diagnosis unit, installed inside the motor control cabinet, which is used to collect the three-phase voltage and current signals of the motor and perform real-time fault feature extraction; An asynchronous motor precision inspection communication module, which is used to transmit the monitoring data and processing results of the in-situ diagnosis unit to the data storage module; An asynchronous motor precision inspection local alarm unit, which is used to perform in-situ sound and light alarm when motor fault symptoms are detected; The in-situ diagnosis unit obtains the three-phase voltage and current of the motor through the secondary side signals of the voltage transformer and the current transformer. Under different load and power supply voltage fluctuation conditions, the inter-turn fault of the motor stator winding, the rotor broken bar fault and the bearing fault are identified by the negative sequence apparent impedance filter value and the stator current modulus spectrum method.

3. An integrated power generation equipment health status supervision system as described in claim 1, characterized in that, The obtaining of the operation parameters and environmental parameter data of the power generation equipment includes: The environmental parameters include obtaining the temperature information collected by the temperature sensor. The temperature sensor is attached to the high-voltage switch contact or bus joint, and the sampling frequency is not less than 1 kHz; The operation parameters include obtaining the mechanical vibration and micro-crack acoustic wave signals collected by the vibration sensor. The vibration sensor integrates a MEMS gyroscope and an acoustic emission probe, and is used to monitor the motor bearing status; The operation parameters also include the three-phase current waveform data collected by the current transformer and the three-phase voltage waveform data collected by the voltage transformer.

4. An integrated power generation equipment health status supervision system as described in claim 1, characterized in that, The data storage module includes: A real-time database, which is used to store the monitoring data of the most recent time or continuously for a short period of time; A historical database, which is used to store long-term monitoring data in time series to support trend analysis, fault traceability and health modeling; The data interface management unit is used to receive the raw data from the multi-source data access module, perform standardized processing on it, and write it into the real-time database.

5. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that the multi-source data access module realizes data integration in the following ways: Time series alignment is performed on the battery monitoring data collected by the TLI-X8 battery online monitoring system; Spectral analysis technology is used for the dissolved gas data in transformer oil collected by the on-line monitoring system for dissolved gases and micro water in transformer oil; High-frequency signal filtering and feature extraction are performed on the partial discharge data collected by the generator partial discharge on-line monitoring device.

6. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that the fault diagnosis and evaluation module includes: Using the mother convolutional neural network model, feature extraction calculations are performed on the input vector composed of historical and real-time input voltage, current, negative sequence impedance, harmonics, temperature, and motor load conditions, and the health evaluation level is output to improve the sensitivity and robustness of fault diagnosis; The data input into the mother convolutional neural network model also includes the feature data calculated by the fault diagnosis algorithm library.

7. An integrated power generation equipment health status monitoring system as described in claim 6, characterized in that the fault diagnosis algorithm library is based on the following methods: For stator winding inter-turn faults, negative sequence current component analysis is used to generate the first analysis data; For rotor bar breaking faults, the detection of the (1±2s)*f frequency component in the stator current spectrum is used to generate detection data; where s represents the slip ratio of the asynchronous motor and f is the power supply fundamental frequency; Bearing faults generate the second analysis data through the joint time-frequency domain analysis of vibration signals.

8. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that the warning levels of the warning module include: three levels of real-time alarm, warning, and prompt information, corresponding to different processing strategies for emergency situations, suspected faults, and normal states respectively.

9. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that the visual human-computer interaction module also includes a digital twin module, which real-timely displays the equipment operation status through 3D modeling and simulates the fault evolution process.

10. An integrated power generation equipment health status monitoring system as described in claim 9, characterized in that the digital twin module supports the following functions: Dynamic visualization of equipment configuration; Superposition display of infrared thermal imaging and real-time data; Matching of historical fault case libraries and recommendation of maintenance strategies.

11. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that it also includes a data interface of the SCADA system to realize seamless integration of the partial discharge intensity PDI signal and temperature data.

12. An integrated power generation equipment health status monitoring system as described in claim 1, characterized in that The precise inspection and diagnosis of asynchronous motors includes the following steps: Real-timely collect the stator current, voltage, and vibration signals of the motor; Extract the negative sequence impedance characteristics and spectral characteristics; Judging the fault risk level of specific fault modes through sub - machine learning models; the specific fault modes include inter - turn short - circuit, rotor bar breakage, and bearing faults; Generating maintenance suggestions and pushing them to the user terminal.

13. An integrated power generation equipment health status supervision system according to claim 12, characterized in that, The sub - machine learning model adopts a hybrid architecture of a deep neural network and a support vector machine; the mother convolutional neural network model and the sub - machine learning model work in parallel or sequentially on the background server or the same hardware platform, and their data streams and operation processes are independent and there is no conflict.

14. An integrated power generation equipment health status supervision system according to claim 1, characterized in that, The visual human - machine interaction module also displays maintenance suggestions including: The fault location accuracy is not less than 95%; Recommendation of maintenance time window; Spare part inventory matching information.

15. An integrated power generation equipment health status supervision system according to claim 12, characterized in that, The sub - machine learning model adopts a multi - modal meta - learning enhanced architecture, including: Physical Constrained Neural Network PC - DNN, embedding the motor electromagnetic - mechanical coupling equation as a physical constraint layer in the deep neural network, and realizing it in the following ways: In the stator winding fault diagnosis branch, the Maxwell's equations are discretized into differentiable operators to force the network output to satisfy the electromagnetic field distribution law; In the bearing fault diagnosis branch, the stress distribution calculated by the Hertz contact theory is injected into the convolution kernel weight initialization as prior knowledge.

16. An integrated power generation equipment health status supervision system according to claim 15, characterized in that, The sub - machine learning model also includes: Dynamic Graph Attention Support Vector Machine DGAT - SVM, constructing a dynamic graph structure based on the equipment operation topology relationship, where: The node features include real - time current harmonic distortion rate, vibration spectrum entropy value, and temperature gradient; The edge weights are dynamically updated according to the electrical connection strength between devices and the historical fault propagation probability; Focus on the key fault correlation paths through the attention mechanism and output the device - level and system - level risk scores.

17. An integrated power generation equipment health status supervision system according to claim 15, characterized in that, The sub - machine learning model also includes: Cross - domain incremental learning module, including: Simulation - measured data alignment unit, using the Adversarial Domain Adaptation (ADA) technology to eliminate the feature distribution deviation between the simulation model and the real device; Online knowledge distillation pipeline, encoding the expert diagnosis rules in the historical fault case library into a lightweight decision tree and outputting for consistency constraint.

18. An integrated power generation equipment health status supervision system according to claim 15, characterized in that, The sub - machine learning model also includes: Uncertainty quantification engine, evaluating the credibility of the diagnosis results in the following ways: Applying Monte Carlo Dropout perturbation to the output of PC - DNN and calculating the confidence interval of the fault probability distribution; Introducing a fuzzy membership function in DGAT - SVM to quantify the progressive process of the device state deviating from the normal threshold.

19. An integrated power generation equipment health status supervision system according to claim 15, characterized in that, The training data of the sub-machine learning model further includes: A multi-physical field coupling fault data set based on finite element simulation, covering rotor eccentricity and insulation aging composite fault scenarios; Equipment full life cycle carbon footprint data for correlating the relationship between fault modes and energy efficiency degradation.

20. An integrated power generation equipment health status supervision system according to claim 1, characterized in that the wireless communication module includes a heterogeneous communication gateway, supports dual-mode communication of LoRa low-power wide area network and 5G network, and adopts a dynamic routing algorithm based on the device topology structure.

21. An integrated power generation equipment health status supervision system according to claim 1, characterized in that The activation function adopted by the mother convolutional neural network model is expressed as follows: ; Activation function adopted by the sub-machine learning model physical constraint neural network is expressed as follows: ; Among them, represents the input value of the current neuron, where e is the base of the natural logarithm; s represents the slip rate of the asynchronous motor, and f is the fundamental frequency of the power supply. represents the frequency offset on the electrical side monitored in real time, that is, the difference from the nominal frequency of 50 Hz; is the dynamic change on the environmental side, that is, the sum of the normalized temperature, wind speed, and load changes; is the coupling coefficient, and the larger the value, the more sensitive it is to the changes in the dynamic characteristics of the motor.

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