Integrated power generation equipment health status monitoring system

Abstract

The present invention provides an integrated power generation equipment health status monitoring system. This system collects operating and environmental parameter data from an asynchronous motor precision inspection and diagnostic device, a TLI-X8 online battery monitoring system, a collector ring intelligent data acquisition system, an online transformer oil dissolved gas and water monitoring system, an online generator partial discharge monitoring device, and a wireless temperature measurement system. This system then performs training and prediction to obtain fault prediction data, enabling accurate fault diagnosis based on deep learning to extract raw data features. This application organically combines systems such as the asynchronous motor precision inspection and diagnostic device, transformer oil gas analysis, partial discharge detection, wireless temperature measurement, and online battery monitoring. This system uses a parent convolutional neural network model and a child machine learning model to predict power generation equipment faults, enabling early fault identification and significantly improving the accuracy and efficiency of equipment fault identification.

Description

Technical Field

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

[0002] The power industry faces significant challenges in ensuring the safe operation and stable power supply of power generation equipment, including high operation and maintenance costs, difficulty in predicting equipment failures, and fragmented system data. To monitor the health of key components, many power generation companies have deployed multiple independent online monitoring or diagnostic systems, such as online battery monitoring systems, intelligent data acquisition systems for collector rings, online monitoring systems for dissolved gas and moisture in transformer oil, online monitoring devices for partial discharge in generators, and wireless temperature measurement systems. These systems can collect and perform partial data analysis on the equipment within their respective monitoring areas, but the lack of a unified integration platform often creates information silos. Operations and maintenance personnel must simultaneously manage multiple software or terminals, preventing comprehensive management from a single platform. Furthermore, cross-system data correlation analysis and comprehensive equipment health assessments are difficult to conduct, hindering the timely identification of potential faults. Furthermore, as power plant operating conditions become increasingly complex, traditional "periodic maintenance" or "planned maintenance" models often suffer from "missed" or "over-inspection" issues when faced with rapidly fluctuating loads and frequent peak-shaving operations. Failure to inspect will bring serious safety hazards and cause unplanned downtime, while over-inspection will lead to repeated disassembly and inspection of equipment, wasting manpower and spare parts resources.

[0003] At the same time, core auxiliary equipment such as asynchronous motors play a vital role in power plants. However, under high loads and frequent starts and stops, they are prone to progressive failures such as stator winding interturn faults, rotor bar failures, and bearing failures. If these failures are not detected at an early stage, they can easily escalate into major accidents, causing widespread downtime and significant economic losses. Although local monitoring methods exist that can collect motor voltage, current, vibration, and other signals, single-metric monitoring strategies struggle to accurately and comprehensively reflect the fault mechanisms and provide maintenance personnel with timely, refined predictions and warnings.

[0004] On the other hand, with the rapid development of information technology and the Internet of Things (IoT), unifying various monitoring devices within power plants into a comprehensive platform and integrating and analyzing massive amounts of historical and real-time data has become a common concern within the industry. Only by interconnecting production monitoring systems can we leverage multi-source, heterogeneous data for in-depth analysis and make rapid, reliable early warning decisions when faults are detected. In recent years, some power plants have attempted to establish equipment health management platforms that integrate and visualize unit operating parameters and monitoring alarm information. However, implementation has been plagued by significant system heterogeneity and inconsistent data standards and interface protocols, resulting in high integration costs and limited scalability, making it difficult to adapt to the subsequent integration of new monitoring equipment. Furthermore, traditional fault diagnosis methods based on static thresholds or fixed models cannot adequately address the dynamic changes in motor loads and environmental conditions, thus limiting the further development of intelligent equipment diagnosis and precise operation and maintenance. Moreover, in the existing technology, all power generation equipment uses the same prediction model as a system for fault diagnosis, and there is no hybrid identification model for the overall system and sub-critical systems, resulting in low fault diagnosis accuracy; and the existing prediction model only predicts the training of data, but does not take into account equipment parameter factors in the network activation function, and does not add a physical constraint layer, resulting in a greatly reduced prediction accuracy, and there is no identification method for key intermediate data as input, which makes the equipment health status prediction efficiency and accuracy low.

[0005] In summary, building a comprehensive monitoring platform that can integrate the monitoring devices already deployed in a power plant and perform high-precision fault identification and real-time health assessments on key equipment such as motors has become a key requirement for ensuring the safe and stable operation of power generation equipment. Accessing a multi-source monitoring system through a unified hardware interface and software platform, supplemented by advanced algorithms for multi-dimensional fault diagnosis and situational awareness, is expected to significantly reduce operation and maintenance costs and improve equipment health management, 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 monitoring system that can organically combine systems such as asynchronous motor precision inspection and diagnostic equipment, transformer oil gas analysis, partial discharge detection, wireless temperature measurement, and battery online monitoring. On this basis, it provides a visual human-machine interaction interface and intelligent early warning mechanism to achieve early fault identification and lean management of the equipment throughout its life cycle. Summary of the Invention

[0006] In response to the above-mentioned problems mentioned in the prior art, the present invention provides an integrated power generation equipment health status monitoring system, which includes a multi-source data access module, a wireless communication module, a data storage module, a fault diagnosis and evaluation module, an early warning module, and a visual human-computer interaction module. By collecting operating parameters and environmental parameter data of an asynchronous motor precision inspection and diagnosis device, a TLI-X8 battery online monitoring system, a collector 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, training and prediction are performed to obtain fault prediction data. This application organically combines systems such as asynchronous motor precision inspection and diagnosis devices, transformer oil gas analysis, partial discharge detection, wireless temperature measurement, and battery online monitoring, and predicts power generation equipment faults through a parent convolutional neural network model and a child machine learning model to achieve early fault screening, greatly improving the accuracy and efficiency of equipment fault identification.

[0007] The present application provides an integrated power generation equipment health status monitoring system, comprising:

[0008] Multi-source data access module, used to access asynchronous motor precision inspection and diagnosis equipment, TLI-X8 battery online monitoring system, slip ring intelligent data acquisition system, transformer oil dissolved gas and trace water online monitoring system, generator partial discharge online monitoring device and wireless temperature measurement system, to obtain operating parameters and environmental parameter data of power generation equipment;

[0009] A wireless communication module transmits the collected operating parameter and environmental parameter data to a data storage module;

[0010] A data storage module, used for storing the operating parameters and environmental parameter data from the multi-source data access module;

[0011] The fault diagnosis and assessment module is used to use the parent convolutional neural network model to perform global training on the acquired operating parameters and environmental parameter data of the power generation equipment to calculate and output the health assessment level, and uses the child machine learning model to calculate and judge specific fault modes and output the fault risk level;

[0012] An early warning module is used to generate an alarm signal when the health assessment level or the fault risk level is lower than the corresponding set threshold;

[0013] The visual human-computer interaction module is used to display the operating status, historical records, alarm information and health assessment level of power generation equipment on the platform interface or mobile terminal.

[0014] Preferably, the asynchronous motor precision spot inspection and diagnostic device comprises:

[0015] The local diagnosis unit is installed inside the motor control cabinet and is used to collect the three-phase voltage and current signals of the motor and perform real-time fault feature extraction;

[0016] an asynchronous motor precision inspection communication module, configured to transmit the monitoring data and processing results of the local diagnosis unit to the data storage module;

[0017] Asynchronous motor precision inspection local alarm unit, used to generate local sound and light alarms when signs of motor failure are detected;

[0018] The local 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 supply voltage fluctuation conditions, the local diagnosis unit identifies the motor stator winding inter-turn fault, rotor bar broken fault and bearing fault by using the negative sequence apparent impedance filter value and stator current modulus spectrum method.

[0019] Preferably, the obtaining of operating parameters and environmental parameter data of the power generation equipment includes:

[0020] Environmental parameters include temperature information collected by temperature sensors attached to high-voltage switch contacts or busbar connectors, with a sampling frequency of no less than 1 kHz;

[0021] The operating parameters include mechanical vibration and micro-crack acoustic wave signals acquired by the vibration sensor, which integrates a MEMS gyroscope and an acoustic emission probe to monitor the motor bearing status;

[0022] The operating parameters also include three-phase current waveform data collected by the current transformer and three-phase voltage waveform data collected by the voltage transformer.

[0023] Preferably, the data storage module includes:

[0024] Real-time database, used to store the most recent or continuous monitoring data within a short period of time;

[0025] Historical database, used to store long-term monitoring data in time series to support trend analysis, fault tracing and health modeling;

[0026] The data interface management unit is used to receive the original data from the multi-source data access module and write it into the real-time database after standardization.

[0027] Preferably, the multi-source data access module realizes data integration by:

[0028] Time series alignment is used on the battery monitoring data collected by the TLI-X8 battery online monitoring system;

[0029] The spectral analysis technology is used to analyze the dissolved gas data in transformer oil collected by the online monitoring system for dissolved gas and trace water in transformer oil;

[0030] High-frequency signal filtering and feature extraction are used to the partial discharge data collected by the generator partial discharge online monitoring device.

[0031] Preferably, the fault diagnosis and evaluation module includes:

[0032] A mother convolutional neural network model is used to extract and calculate features from input vectors consisting of historical and real-time input voltage, current, negative sequence impedance, harmonics, temperature, and motor load conditions, and output a health assessment level to improve the sensitivity and robustness of fault diagnosis.

[0033] The data input to the mother convolutional neural network model also includes feature data calculated and obtained by the fault diagnosis algorithm library.

[0034] Preferably, the fault diagnosis algorithm library is based on the following method:

[0035] The stator winding inter-turn fault adopts negative sequence current component analysis to generate first analysis data;

[0036] The rotor bar broken fault is detected by detecting the (1±2s)*f frequency component in the stator current spectrum to generate detection data; where s represents the slip rate of the asynchronous motor and f is the fundamental frequency of the power supply;

[0037] The bearing fault generates the second analysis data through the joint analysis of the vibration signal in time and frequency domains.

[0038] Preferably, the warning levels of the warning module include three levels: real-time alarm, warning and prompt information, which correspond to different processing strategies for emergency situations, suspected faults and normal states respectively.

[0039] 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.

[0040] Preferably, the digital twin module supports the following functions:

[0041] Dynamic visualization of equipment configuration;

[0042] Infrared thermal imaging and real-time data superposition display;

[0043] Matching historical fault case libraries and recommending maintenance strategies.

[0044] Preferably, it also includes a data interface of the SCADA system to achieve seamless integration of partial discharge intensity PDI signals and temperature data.

[0045] Preferably, the asynchronous motor precision spot check diagnosis includes the following steps:

[0046] Real-time collection of motor stator current, voltage and vibration signals;

[0047] Extract negative sequence impedance characteristics and spectrum characteristics;

[0048] Determine the risk level of specific fault modes using a sub-machine learning model; specific fault modes include turn-to-turn short circuits, broken rotor bars, and bearing failures;

[0049] Generate maintenance recommendations and push them to user terminals.

[0050] Preferably, the sub-machine learning model adopts a hybrid architecture of deep neural network and support vector machine; the parent 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 flow and calculation process remain independent without conflict.

[0051] Preferably, the visual human-computer interaction module further displays maintenance suggestions including:

[0052] Fault location accuracy is not less than 95%;

[0053] Maintenance time window recommendation;

[0054] Spare parts inventory matching information.

[0055] Preferably, the sub-machine learning model adopts a multimodal meta-learning enhanced architecture, including:

[0056] The physical constraint neural network (PC-DNN) embeds the motor electromagnetic-mechanical coupling equations in a deep neural network as a physical constraint layer. This is achieved through the following methods:

[0057] In the stator winding fault diagnosis branch, the Maxwell equations are discretized into differentiable operators, forcing the network output to satisfy the electromagnetic field distribution law;

[0058] In the bearing fault diagnosis branch, the stress distribution calculated by Hertz contact theory is injected into the convolution kernel weight initialization as prior knowledge.

[0059] Preferably, the sub-machine learning model further includes:

[0060] Dynamic graph attention support vector machine DGAT-SVM builds a dynamic graph structure based on the device operation topology relationship, where:

[0061] Node characteristics include real-time current harmonic distortion rate, vibration spectrum entropy value and temperature gradient;

[0062] Edge weights are dynamically updated based on the electrical connection strength between devices and the historical fault propagation probability;

[0063] The attention mechanism is used to focus on key fault-related paths and output device-level and system-level risk scores.

[0064] Preferably, the sub-machine learning model further includes:

[0065] Cross-domain incremental learning module, including:

[0066] The simulation-measurement data alignment unit uses adversarial domain adaptation (ADA) technology to eliminate feature distribution deviations between the simulation model and the real device.

[0067] The online knowledge distillation pipeline encodes expert diagnosis rules in the historical fault case library into lightweight decision trees and outputs them for consistency constraints.

[0068] Preferably, the sub-machine learning model further includes:

[0069] Uncertainty quantification engine, which assesses the confidence of diagnosis results by:

[0070] Apply Monte Carlo Dropout perturbation to the PC-DNN output and calculate the confidence interval of the failure probability distribution;

[0071] A fuzzy membership function is introduced into DGAT-SVM to quantify the gradual process of equipment status deviating from the normal threshold.

[0072] Preferably, the training data of the sub-machine learning model further includes:

[0073] A multi-physics coupled fault dataset based on finite element simulation, covering rotor eccentricity and insulation aging combined fault scenarios;

[0074] The carbon footprint data of the equipment throughout its life cycle is used to correlate failure modes with energy efficiency degradation.

[0075] 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 device topology.

[0076] Preferably, the activation function used by the mother convolutional neural network model is It is expressed as follows: ;

[0077] Activation function used by the neural network in the physical constraint sub-machine learning model It is expressed as follows: ;

[0078] in, 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, Indicates the real-time monitored frequency offset on the electrical side, that is, the difference from the nominal frequency of 50Hz; is the dynamic change of the environment side, that is, the normalized sum of temperature, wind speed and load changes; is the coupling coefficient. A larger value indicates greater sensitivity to changes in the motor's dynamic characteristics.

[0079] The present invention provides an integrated power generation equipment health status monitoring system, which can achieve the following beneficial technical effects:

[0080] This invention organically integrates traditionally decentralized systems for online battery monitoring, intelligent data collection for collector rings, dissolved gas analysis in transformer oil, partial discharge monitoring, wireless temperature measurement, and precision inspection and diagnosis of asynchronous motors. Through a unified interface protocol and data management module, it enables centralized acquisition and visual management of multi-source heterogeneous data. This significantly reduces the workload for maintenance personnel who repeatedly switch between different systems, avoids information silos, and lays a solid foundation for subsequent system expansion and integration. By appending feature data calculated by a fault diagnosis algorithm library (such as negative-sequence current components and spectral characteristics) to the input layer of the parent convolutional neural network model, the invention directly incorporates the "prior knowledge" of various classical diagnostic methods, building on the raw data feature extraction through deep learning. This not only further enriches the model input dimension but also ensures the complementary advantages of deep networks and traditional diagnostic features, thereby enhancing both the reliability and sensitivity of fault detection and enabling more accurate and timely identification of early fault signs.

[0081] 2. The present invention utilizes a fault diagnosis and assessment module, relying on advanced algorithms such as a fusion of a parent convolutional neural network and a physical constraint sub-network, to rapidly identify early signs of motor stator winding interturn faults, broken rotor bars, and bearing faults under complex operating conditions. Furthermore, the system significantly improves the sensitivity and accuracy of fault identification by combining techniques such as negative-sequence apparent impedance, stator current modulus spectrum, and multimodal meta-learning. Once the health assessment level is detected to be below a threshold, the system automatically triggers an early warning and pushes maintenance recommendations to prevent further deterioration of the fault and unplanned downtime. The present invention uses a parent convolutional neural network model and a sub-machine learning model to predict power generation equipment faults, enabling early fault identification and significantly improving the accuracy and efficiency of equipment fault identification. The present invention rationally incorporates physical or operating condition information, such as motor slip and environmental interference coefficient, into the activation function of the parent convolutional neural network model, eliminating the input of the activation function as a simple linear transformation. This allows the network to adaptively adjust its output sensitivity in the face of changing operating conditions. This improved activation function not only enhances the robustness to complex factors such as electrical side imbalance and load fluctuation, but also effectively prevents gradient vanishing or gradient explosion, thereby shortening the convergence time while maintaining high-precision diagnosis.

[0082] 3. This invention adds a physical constraint neural network (PC-DNN) to the sub-machine learning model and embeds the motor's electromagnetic-mechanical coupling equations into the deep network. This allows the network to not only rely on data during training but also impose constraints on the network output through physical equations, forcing it to conform to the internal physical laws of the motor. This significantly reduces the reliance on large-scale annotated data, improves prediction accuracy and stability under complex operating conditions or when insufficient samples are available, and effectively prevents network outputs from being inconsistent with the actual physical mechanisms, greatly enhancing the reliability and interpretability of fault diagnosis. Introducing a dynamic graph attention support vector machine within a multimodal meta-learning architecture allows for the construction of a dynamic graph structure model based on the operating topology of devices, dynamically updating node and edge weights. Furthermore, the attention mechanism focuses on key fault correlation paths, enabling accurate identification of global fault propagation links. This combination significantly improves the ability to assess system-level fault risks. It can quickly extract key features in scenarios with complex fault correlations across multiple devices, reducing interference from irrelevant information, thereby significantly enhancing the overall system's fault prediction performance and real-time response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 It is a schematic diagram of an integrated power generation equipment health status monitoring system of the present invention;

[0085] Figure 2 This is a schematic diagram of an online diagnosis and early warning workstation for initial motor faults of the present invention;

[0086] Figure 3 It is a schematic diagram of the motor state monitoring and initial fault warning system of the present invention. DETAILED DESCRIPTION

[0087] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0088] Example 1:

[0089] In order to solve the above-mentioned technical problems mentioned in the prior art, the following Figure 1 As shown, this embodiment provides an integrated power generation equipment health status monitoring system, which may include an asynchronous motor precision inspection and diagnosis device, a TLI-X8 battery online monitoring system, a slip ring intelligent data acquisition system, a wireless temperature measurement system, a generator partial discharge online monitoring device, a transformer oil dissolved gas and trace water online monitoring system, a computing unit, an alarm unit, and a platform interface or client terminal (such as a PC, mobile phone APP).

[0090] The aforementioned asynchronous motor precision inspection and diagnostic device, TLI-X8 online battery monitoring system, intelligent data acquisition system for collector rings, wireless temperature measurement system, online generator partial discharge monitoring device, and online transformer oil dissolved gas and moisture monitoring system all use transformer oil dissolved gas sensors to acquire real-time raw data such as electrical, temperature, vibration, and gas concentration data, and transmit it via wired or short-range wireless (RS485, CAN, LAN, etc.) channels. The computing unit is responsible for data aggregation, storage, computation, and communication, and is internally divided into four core modules: a) LoRa / 5G: responsible for low-power wide-area / high-speed data transmission from remote or distributed devices; b) Data storage: including a real-time database and a historical database for caching high-frequency sampling values and long-term trend data; c) CPU / GPU: deploying a parent convolutional neural network and child machine learning models to implement health scoring, fault identification, and maintenance window prediction; d) Field switch / gateway: connecting to the factory's industrial Ethernet network, transferring data from various monitoring devices and LoRa / 5G links to the computing unit and distributing model parameters or control instructions downward. The above modules are interconnected through high-speed buses or Gigabit Ethernet to ensure millisecond-level data flow. All monitoring data first enters the data storage, and then the CPU / GPU completes parallel or sequential reasoning; the risk value and health index 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 calculation unit reaches the set threshold, it immediately triggers the local sound and light alarm, SMS / APP push or relay protection action to ensure timely and effective accident prevention. The platform interface or client terminal can be a PC, mobile phone APP, etc., which calls the calculation unit database through a standard API to visualize real-time curves, digital twin 3D 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.

[0091] Specifically, the asynchronous motor precision inspection and diagnosis device may 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 the motor has serious fault signs, it will issue an audible and visual warning on-site; the communication module in the computing unit uploads the collected and diagnostic data to the gateway via RS485, industrial Ethernet or LoRa / 5G, and the communication module may include Figure 1 The LoRa / 5G TLI-X8 online battery monitoring device features a built-in sensor module that monitors battery voltage, internal resistance, temperature, and other parameters. A generator partial discharge online monitoring device (such as the W-PD6) includes a high-frequency sensor and an acquisition front-end for amplifying and filtering partial discharge pulse signals. A wireless temperature measurement system (such as the XK-WCX) includes a wireless temperature sensor attached to high-voltage contacts or other hot spots to periodically report temperature data. An online monitoring system for dissolved gas and water in transformer oil (such as TRANSFIX™) includes a photoacoustic spectroscopy unit that separates and detects gas concentration and water content in the oil. The intelligent data acquisition system for slip rings collects carbon brush current radar images, temperature, vibration, and other status information through a ring-side device. Alarm push notifications can be sent via the platform interface or client terminals (such as PCs or mobile apps). Operations and maintenance personnel can receive these alerts, view fault diagnosis details, and log on-site inspections.

[0092] In some embodiments, the system may also include a gateway / interface module that packages monitoring data and uploads it via a LAN or wireless network. The interface module connects to a higher-level system and typically supports industrial protocols such as Modbus TCP / OPC UA. The communication gateway uploads the acquired discharge intensity (PDI) or pulse characteristics to the computing unit. A centralized receiving gateway interacts with the computing unit via industrial Ethernet or wirelessly. Field switches / hubs aggregate the network ports of monitoring devices, such as asynchronous motor precision inspection and diagnostic devices, the TLI-X8 online battery monitoring system, and the slip ring intelligent data acquisition system, onto a factory LAN or dedicated industrial Ethernet network. Wireless communication modules or IoT gateways are deployed in applications such as LoRa / 5G that require wide-range, low-power, or high-speed transmission to ensure stable access for all devices.

[0093] The data storage system 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 parent convolutional neural network model and child machine learning models. The platform interface provides a visual human-computer interaction interface, a digital twin module, and alarm management and reporting capabilities. The client terminal allows on-duty personnel to view the status of multiple devices on a centralized monitoring screen.

[0094] In some embodiments, the above system may also include: a multi-source data access module, which 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 transformer oil dissolved gas and micro-water online monitoring system, the generator partial discharge online monitoring device and the wireless temperature measurement system to obtain the operating parameters and environmental parameter data of the power generation equipment.

[0095] In some embodiments, the above system may further include: an online generator insulation overheating monitoring device and a hydrogen dryer monitoring module for monitoring generator insulation overheating and humidity of the output gas of the hydrogen dryer device.

[0096] Among them, the temperature, humidity, dew point, alarm signs and other information collected by the generator insulation overheating online monitoring device and the hydrogen dryer monitoring module are connected to the multi-source data access module through a unified protocol or gateway, and are summarized into the calculation unit together with the collected data of the TLI-X8 battery online monitoring device, the transformer oil dissolved gas and trace water online monitoring system, the generator partial discharge online monitoring device, the wireless temperature measurement system and other devices.

[0097] The generator insulation overheating online monitoring device is integrated into the generator's hydrogen cooling system through dedicated piping. It assesses the generator's operating condition by monitoring the hydrogen for insulation decomposition products caused by overheating. The device is connected to the generator's interior via an inlet and outlet, forming a closed circulation system. Under the pressure of the generator's fan, cooling gas enters the device's ion chamber detector. Bombarded by alpha radiation emitted by the radioactive Am241 source within the ion chamber, the cooling gas ionizes, generating positive and negative ion pairs (hydrogen ion pairs in hydrogen-cooled units). A DC electric field is then applied to the ion chamber, causing the positive and negative ion pairs to migrate in a directional manner, generating an extremely weak ionization current (10-12A). This current is amplified (1010 times) by an amplifier and displayed on a liquid crystal display.

[0098] In some embodiments, a generator insulation overheating online monitoring device not only identifies the insulation status by monitoring the presence of insulation decomposition products (such as acetylene, methane, and carbon dioxide) in the hydrogen cooling system, but also comprehensively assesses the operating condition of the generator insulation system by combining parameters such as decomposition product concentration trends, hydrogen humidity, temperature rise rate, and operating load. The specific implementation method is as follows: First, the device incorporates a highly sensitive gas sensor module that uses infrared absorption spectroscopy or a gas sensor array to periodically sample changes in trace gas concentrations in the hydrogen flow. The sensor signal is processed by a current amplification circuit (with an amplification factor of, for example, 1010), converted into a digital signal, and transmitted to the monitoring control unit. The control unit incorporates a built-in multi-parameter analysis algorithm that fits the decomposition gas concentration growth rate within a specific time window to extract a characteristic curve for the insulation thermal decomposition trend. Second, the system cross-compares this data with the generator's current operating load, cooling gas temperature, and generator-end temperature rise. When the concentration of a certain type of decomposition gas (such as CO2 or C2H2) is detected to be continuously increasing, and at the same time, the hydrogen humidity is increasing, the temperature rise rate is abnormal, or the unit is operating in a high-load range for a long time, the system can determine that the current generator is in a state of mild insulation thermal aging or overheating warning; if the gas concentration jumps, the temperature rise slope increases sharply, and is accompanied by other abnormal signals (such as an increase in partial discharge intensity), it is determined to be an insulation overheating risk or a precursor to breakdown. In addition, to avoid misjudgment, the system has a built-in dynamic alarm threshold adjustment mechanism, which dynamically corrects the alarm limit according to operating conditions (such as start-stop frequency, cooling air volume, and ambient temperature) to ensure accurate response in different power plant on-site environments. The monitoring results are displayed on the LCD screen as insulation risk level (normal, suspicious, serious) and are simultaneously uploaded to the SCADA system for remote analysis and alarm linkage.

[0099] The generator insulation overheating online monitoring device is mainly used to monitor the risks of high temperature, hot spots, degradation, etc. that appear in the insulation layer of the generator stator winding or rotor coil during operation in real time. 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 area inside the motor; Temperature gradient: By comparing the temperature difference or temperature rise rate of multiple points in 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 interfaces, local data processing: Some monitoring devices can perform temperature curve analysis locally and generate overheat warning indicators. Communication methods: RS485 / Modbus, Ethernet, or fiber optic networks are typically used to connect to the host system, sending temperature measurements and gradient alarm information to the multi-source data access module. Integration with other systems: The insulation overheat alarm signal can be correlated and analyzed with the discharge intensity (PDI) data from the generator partial discharge online monitoring device to form a comprehensive judgment on the generator insulation fault. Prevention of insulation aging failure: Real-time monitoring of actual temperature changes in the windings or rotor coils helps to detect thermal degradation of the insulation layer or local overheating spots early. Improved safety and economic efficiency: If a hot spot heats up abnormally, the system can promptly issue an alarm to prompt operators to take measures such as maintenance or other protective measures, thereby reducing losses from unplanned downtime.

[0100] The hydrogen dryer device is mainly used for hydrogen purification and drying in large steam turbine generators 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 hydrogen drying tanks, adsorption materials, temperature and humidity sensors, and automatic control valves, and realizes continuous dehumidification of hydrogen through 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 a built-in or external humidity transmitter to determine the drying effect; Hydrogen purity: Some hydrogen dryer devices are equipped with gas analyzers to measure the impurities 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 interface, automatic control: Through a PLC or embedded control unit, the hydrogen dryer device can automatically switch working states based on humidity sensor and hydrogen flow feedback to ensure that the hydrogen remains dry for a long time; data upload: humidity values, dew point, pressure, purity and other indicators are sent to the multi-source data access module on a scheduled or real-time basis. The communication method can adopt industrial Ethernet, RS485 / Modbus or dedicated protocols; 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 host system, reminding the operation and maintenance personnel to replace or repair it in time. Suppress insulation moisture: Effectively reduce the moisture content of the hydrogen cooling system, reducing the risk of failure caused by insulation moisture; Improve generator cooling efficiency: Ensuring high hydrogen purity helps to enhance thermal conductivity and reduce heat generation, thereby maintaining the generator operating in a high-efficiency range; Extend equipment life: In conjunction with the generator insulation overheating online monitoring device, the system can more comprehensively grasp the cooling status and insulation health, making it easier to detect and deal with potential faults early.

[0101] In some embodiments, the integrated power generation equipment health status monitoring system may also include: sensors / transformers, which may include temperature sensors and vibration sensors / acoustic emission probes; the temperature sensor is attached to the high-voltage switch contacts or busbar connectors to monitor the temperature and provide the upper system with equipment heating conditions; the vibration sensor / acoustic emission probe is installed on the motor bearings or other key parts to collect mechanical vibration and sound wave signals; the transformer may include a current transformer and a voltage transformer, and the current transformer (CT) outputs a 1A signal through the secondary side to achieve safe collection of the three-phase current waveform; the voltage transformer (PT) converts the high voltage into a processable low-voltage signal to facilitate accurate measurement of the motor or bus voltage waveform.

[0102] To connect sensors / transformers to the local diagnostic unit in the precision inspection and diagnostic system for asynchronous motors, cables are used to connect signals from the PT and CT secondary sides, as well as vibration / acoustic emission probes, to the local diagnostic unit. The local diagnostic unit collects data and performs preliminary calculations (such as negative-sequence impedance or spectral characteristics) and internally buffers the data as needed. For connection to a field switch / gateway, if the local diagnostic unit has an industrial Ethernet port, it can be directly connected to the field switch, which then transmits the data to the calculation unit via the factory LAN. If the field environment is more suitable for RS485 / Modbus RTU or wireless communication, the calculation unit can be connected via a field switch or gateway. For connection to the calculation unit, specialized monitoring devices (such as the TLI-X8 online battery monitoring system, the online transformer oil dissolved gas and water monitoring system, and the online generator partial discharge monitoring system) are typically uploaded using Ethernet (TCP / IP), RS485 / Modbus, CAN bus, or wireless solutions. The multi-source data access module parses and standardizes the data before writing it to a database in the data storage system. Inside the computing unit, the data storage and CPU / GPU are interconnected via a high-speed LAN or storage network. The CPU / GPU reads monitoring data from the database in real time or directly subscribes to sensor data streams to run the parent CNN and child machine learning models. Processing / analysis results are shared with the platform interface or client terminal (such as a PC or mobile app) via WebSocket, REST API, or industrial protocol interface. For connections between the alarm unit and the platform interface or client terminal (such as a PC or mobile app), the alarm unit pushes monitoring curves and alarm information to the control room's large screen or SCADA workstation. At the same time, important alarms are pushed to the platform interface or client terminal (such as a PC or mobile app) via an app or communication software, supporting remote viewing and confirmation.

[0103] In Example 2, the present invention further provides an integrated power generation equipment health status monitoring system, which may include: a multi-source data access module, a wireless communication module, a data storage module, a fault diagnosis and evaluation module, an early warning module, and a visual human-computer interaction module, wherein the multi-source data access module is 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, an online monitoring system for dissolved gas and trace water in transformer oil, an online monitoring device for partial discharge of a generator, and a wireless temperature measurement system to obtain operating parameters and environmental parameter data of the power generation equipment;

[0104] A wireless communication module transmits the collected operating parameter and environmental parameter data to a data storage module;

[0105] A data storage module, used for storing the operating parameters and environmental parameter data from the multi-source data access module;

[0106] The fault diagnosis and assessment module is used to use the parent convolutional neural network model to perform global training on the acquired operating parameters and environmental parameter data of the power generation equipment to calculate and output the health assessment level, and uses the child machine learning model to calculate and judge specific fault modes and output the fault risk level;

[0107] An early warning module is used to generate an alarm signal when the health assessment level or the fault risk level is lower than the corresponding set threshold;

[0108] The visual human-computer interaction module is used to display the operating status, historical records, alarm information and health assessment level of power generation equipment on the platform interface or mobile terminal.

[0109] 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 data acquisition and management of various online monitoring devices through parallel wired and wireless communication networks. Specifically:

[0110] Asynchronous motor precision inspection and diagnostic equipment is connected. The local diagnostic unit in each key asynchronous motor control cabinet is connected to the multi-source data access module via industrial Ethernet or RS485 bus. The three-phase voltage and current collected by the local diagnostic unit, along with fault signature values derived through online diagnostic operations such as negative sequence impedance algorithms and spectrum analysis, are automatically timestamped and uploaded to the multi-source data access module.

[0111] The TLI-X8 online battery monitoring system is integrated. For UPS battery stations, the TLI-X8 system outputs real-time battery voltage, current, temperature, internal resistance, and other parameters through its built-in gateway. The multi-source data access module establishes a connection with the gateway using the Modbus TCP or OPC UA protocol it supports, periodically reading and storing key performance indicators. If a single battery shows signs of degradation, the system can issue a real-time warning.

[0112] The intelligent data acquisition system for the slip rings is integrated. A ring-side device collects carbon brush current radar charts, temperature, vibration, and other status information, which is then transmitted via TCP / IP to a multi-source data access module. To handle high-frequency data, the module incorporates a high-speed cache and performs data compression and preprocessing to reduce pressure on the backend server.

[0113] The TRANSFIX™ device uses a dedicated photoacoustic spectroscopy unit to measure the gas content and moisture content of the transformer's faulty oil. The device then transmits the results to a multi-source data access module via a serial port or Ethernet. The module then standardizes the received gas concentration data and integrates it with the oil management procedures used by the main control system to determine the health of the transformer's insulation.

[0114] The W-PD6 is connected to the generator's online partial discharge monitoring device. The device collects the generator's partial discharge intensity (PDI) and high-frequency discharge waveform. After high-frequency isolation, the signal is transmitted to the data acquisition terminal and then, via fiber-optic Ethernet or CAN interfaces, to the multi-source data access module. The module compares the real-time partial discharge value with a reference threshold. If an anomaly occurs, an alarm is triggered and pushed to the backend server.

[0115] Wireless temperature measurement system access. The XK-WCX temperature measurement device attaches sensors to high-voltage switch contacts or busbar connectors and transmits temperature data via radio frequency to a centralized gateway. The gateway then integrates with the multi-source data access module via Ethernet. The multi-source data access module correlates real-time temperature measurements with parameters such as ambient temperature and load current to identify potential overheating or loose contact faults.

[0116] Through this approach, the multi-source data access module can simultaneously collect electrical parameters (voltage, current, gas content in transformer oil, etc.) and environmental parameters (temperature, vibration, etc.). It then packages and uploads the monitoring data from different devices to the data storage module for unified management. This ensures the normal independent operation of each monitoring system while enabling cross-platform data integration and centralized monitoring. If an abnormality occurs, the system immediately pushes information to the fault diagnosis and assessment module and visualization interface, significantly improving the efficiency of identifying and addressing potential power generation equipment faults.

[0117] The wireless communication module uses IoT technology to achieve remote data transmission. The wireless communication module complements the power plant's internal wired network, utilizing IoT technology to enable secure remote transmission and centralized management of power generation equipment data. In some embodiments, the workflow is generally as follows: The wireless communication module is equipped with a heterogeneous communication gateway to support dual-mode communication using the mainstream Low-Power Wide Area Network (LoRa) and high-speed 5G networks. For dispersed transformer oil dissolved gas sensors and wireless temperature measurement systems, collected key parameters (temperature, trace moisture, fault gas concentration, etc.) are transmitted to a nearby LoRa base station via LoRa networking. The base station then uses the 5G network to package and transmit the data to a multi-source data access module or data storage module. This approach balances low power consumption requirements with high-bandwidth, high-speed, real-time transmission when needed. For on-site TLI-X8 battery online monitoring systems and generator partial discharge online monitoring devices, if wireless transmission is used, the monitoring data is first aggregated into lightweight data packets (such as JSON or binary protocol format) using a built-in RF communication unit or an external IoT gateway, and timestamps, device IDs, and checksums are added. The wireless communication module then performs preliminary encryption or identity authentication on the data packet. Because power generation equipment is often located in locations such as generator areas, switch rooms, and high-voltage rooms, the internal network environment is complex. This embodiment deploys several relay nodes in required areas. When the signal from the wireless temperature measurement system or the asynchronous motor precision inspection and diagnostic device is weak, the relay nodes forward the signal to a unified primary communication gateway, which then transmits it uplink to the power plant data center via an IoT operator base station or a dedicated 5G base station. In the high-voltage equipment area, the communication module automatically switches to a more interference-resistant communication method (such as the 5G NR Sub-6G band or the LoRa 433MHz band) to ensure accurate data transmission. To prevent external interference and network fluctuations, the wireless communication module employs an end-to-end encryption strategy and a polling mechanism on the gateway side. If a transmission anomaly or packet loss is detected, the system immediately retransmits or switches to a backup network. If the network outage lasts for a long time, the module temporarily caches critical data in local flash memory to ensure data is not lost during the outage. Data uplink connects to the monitoring platform. Ultimately, during the IoT data uplink process, the operating and environmental parameters of all on-site equipment are centralized via wireless channels to the power plant data center or forwarded via VPN tunnels to the cloud-based operations and maintenance platform. Maintenance personnel can view key indicators such as temperature, negative sequence impedance, and gas content in oil from the wireless communication link on a visualization terminal, and conduct comprehensive analysis with data sources from the wired network, enabling remote and unified monitoring of equipment across the entire plant.

[0118] A data storage module is used to store monitoring data from the multi-source data access module. In this embodiment, the data storage module is deployed on a server in the power plant data center. Its core comprises a real-time database and a historical database, which are used to hierarchically store and uniformly manage 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 from the asynchronous motor precision inspection and diagnostic device, the online monitoring system for dissolved gas and trace water in transformer oil, or the online monitoring system for partial discharge in generators, the system first writes the newly received data to a real-time database (such as an in-memory database or a high-performance time-series database) and uses a cache queue mechanism to enable rapid access within milliseconds or seconds. Operations and maintenance personnel or application algorithms can directly access this data at the "real-time layer" for fault diagnosis or trend analysis, meeting the requirements of online equipment status monitoring and high-frequency refresh. To support long-term traceability and trend research, the data storage module automatically transfers data from the real-time database to the historical database in the background according to preset cycles or event-triggered rules. This historical database can be based on a relational database (such as PostgreSQL) or a distributed time-series database (such as InfluxDB). For example, all motor three-phase voltage, current, negative sequence current component, fault alarm flags, as well as battery voltage, oil gas concentration, and temperature sample values are uniquely timestamped and device-identified, then stored in a historical database for long-term storage. The data storage module automatically parses and cleans data packets of varying formats and protocols from the multi-source data access module. When receiving TLI-X8 battery monitoring data, it first identifies its JSON data structure and maps fields such as "battery pack ID," "cell voltage," and "internal resistance" to database tables. For transformer oil dissolved gas or partial discharge data, corresponding parsers and feature extraction routines are used to convert the raw sample values into numerical values that align with other device data. After standardization, the data can be retrieved, compared, and analyzed within a unified time series dimension. In this embodiment, to ensure data integrity and traceability, the data storage module maintains a multi-level log: Any operations, such as adding or deleting device point tables or modifying monitoring frequencies, record the operator, time, and content of the change at the database level. Data write operations for all key sensors also generate real-time audit records. When troubleshooting or investigation is needed, operations personnel can accurately locate the source and write time of each piece of data and retrieve the system configuration at the time. For fault diagnosis and assessment modules, visual human-computer interaction modules, or third-party operations and maintenance management platforms, the data storage module in this embodiment provides an open API interface and access control: real-time data can be pushed to the fault assessment algorithm via WebSocket or lightweight message queues for online analysis; historical data can be deeply searched using SQL or time series query languages, facilitating the generation of trend charts, health score reports, and the exploration of operational patterns throughout the equipment lifecycle.

[0119] The fault diagnosis and assessment module, whose central processing unit is responsible for analyzing the operating and environmental parameters of the power generation equipment and generating health assessment results, is deployed in the power plant's data center or cloud server. It incorporates a built-in central processing unit (CPU or GPU acceleration) and multiple intelligent diagnostic algorithms to comprehensively analyze the power generation equipment's operating parameters (voltage, current, negative sequence impedance, vibration signals, etc.) and external environmental parameters (temperature, humidity, load conditions, etc.) and output health assessment results. In some embodiments, the specific process is as follows: data reception and preprocessing: real-time or batch data acquired from the multi-source data access module and data storage module is automatically aggregated into the fault diagnosis and assessment module via a 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 diagnostic device transmits current harmonic components once per second and the temperature sensor uploads data every five seconds, this module uses interpolation to unify the key time axes to facilitate subsequent algorithm synchronization. Feature extraction and fusion: The fault diagnosis and assessment module uses specific feature extraction strategies for different equipment types. For motor stator current, it extracts fault frequency features such as negative-sequence apparent impedance, stator current modulus spectrum, and (1±2s)·f. For vibration signals, it extracts envelope spectrum, signal energy ratio, or joint time-frequency domain features. External environmental data such as temperature, humidity, and load are regularized or normalized to facilitate multimodal fusion. Once all features are integrated into a single multidimensional data structure, the module can perform further intelligent analysis. This embodiment deploys a fault detection algorithm in the central processing unit that combines a parent convolutional neural network model with a child machine learning model. The parent network focuses on deep convolution and time series feature mining of input data (voltage, current, vibration, etc.). The child network utilizes structures such as physically constrained neural networks (PC-DNNs) and dynamic graph attention support vector machines (DGAT-SVMs) to more accurately identify faults based on the multi-dimensional coupling relationships between motors and power plant equipment (such as electromagnetic-mechanical coupling and fault propagation paths between devices). If signs of inter-turn faults in the stator winding are detected, the corresponding fault index, affected coil location, and confidence score are output. If broken rotor bars or abnormal bearing signals are identified, the degradation trend and possible rate of evolution are determined by comparing historical data. After analyzing the fault characteristics, the health assessment and scoring module further assesses the equipment's health status by quantifying its "current health" in the form of a score or grade. Trend forecasts or remaining useful life (RUL) estimates are used to infer whether the equipment is likely to experience fault escalation in the future. This embodiment also provides multi-dimensional assessment views: individual equipment scores, comparative analysis of similar equipment, and the overall unit health index, allowing managers to understand risk distribution at different levels.Diagnostic result output and interaction: When the fault diagnosis and assessment module detects a health score below a certain threshold or obvious signs of a fault, it pushes the results to the early warning module and the visual human-computer interaction interface. If a sharp deterioration is detected, an emergency alarm is triggered and an immediate shutdown and maintenance recommendation is recommended. For minor anomalies, routine inspection recommendations are output or the early warning information is included in the maintenance schedule. The module also records the diagnosis details in a historical database, including fault characteristic components, confidence level, and recommended maintenance timing, to support subsequent review and continuous learning and improvement.

[0120] The early warning module generates an alarm signal when a fault is detected or the health assessment level falls below a set threshold. In some embodiments, the early warning module collaborates with the fault diagnosis and assessment module and is deployed on an application server in the power plant data center. It primarily triggers an alarm signal when a power generation equipment fault is detected or the health assessment level falls below a set threshold, allowing operations and maintenance personnel to take timely maintenance measures. Its workflow is as follows: Threshold configuration and tiered management: Operation and maintenance personnel pre-set corresponding alarm thresholds and tiered strategies in the early warning module's configuration interface based on equipment type, operational importance, and risk level. For example, a health score below 60 for an asynchronous motor triggers a "warning" level, while one below 40 triggers an "emergency" level. For dissolved gases in transformer oil, a high-level alarm is also triggered when the concentration of certain fault gases continuously rises to a predetermined value. Real-time monitoring and judgment: The early warning module receives the health score, fault index, and degradation trend information output by the fault diagnosis and assessment module periodically or in real time. If the score is significantly below the threshold or a serious equipment anomaly is detected (such as strong signs of broken rotor bars or rapid insulation deterioration), the module immediately determines whether the alarm triggering conditions have been met. Minor anomalies are logged and the status updated to "under observation." Anomalies exceeding the critical threshold directly activate the highest-level alarm. Multiple alarm signal outputs are provided. Once an alarm is triggered, the early warning module notifies operations and maintenance personnel and managers at different levels through the following methods: Visual pop-up notifications: A prominent warning message appears on the monitoring screen in the control room or management backend, identifying the faulty device, specific monitoring parameters, and diagnostic recommendations. Mobile device push notifications: Fault details are pushed to relevant personnel via SMS, app, or WeChat, ensuring immediate notification of the anomaly. Local audio and visual alarms: If the field equipment supports localized alarms, the early warning module can also send commands to trigger sirens or warning lights, prompting inspection personnel to take timely action. Alarm processing and closed-loop feedback: Upon receiving an alarm, the on-duty personnel or emergency repair team, based on the alarm level and diagnostic results, determine whether downtime, further investigation, or observational maintenance is required. Upon completion of processing, personnel will provide corresponding maintenance records or inspection results to the early warning module. This information can be stored alongside historical alarm data for subsequent statistical analysis and model optimization.

[0121] A visual human-computer interaction module is used to display the operating status, historical records, alarm information, and health assessment results of power generation equipment on a platform interface or mobile terminal. In some embodiments, the visual human-computer interaction module is deployed on the large-screen monitoring system in the power plant's centralized control room and on mobile terminals (such as tablets or mobile apps) for operators and maintenance personnel, providing multi-dimensional information display and interactive functions for operators and managers. The main implementation steps are as follows: System homepage and equipment overview. On the main interface, operators can switch to the "Equipment Overview" page with a single click, displaying the operating status and health scores of key equipment (asynchronous motors, transformers, generators, batteries, etc.) for the entire plant or each unit. Each device is displayed as a card or icon, with a color or icon indicating whether there are current warnings or faults—for example, green indicates normal operation, yellow indicates a warning, and red indicates a serious fault. Operators click the corresponding icon to access the detailed monitoring page for that device. Real-time operating data and historical records: For asynchronous motors, real-time data displays current voltage, current, negative sequence impedance, and slip; for transformer oil monitoring, displays gas concentration and trace water content in the oil; and for wireless temperature measurement systems, displays real-time temperature curves. The interface features scrolling refreshes using a graph, radar chart, or progress bar, with a customizable update interval between 2 seconds and 1 minute. The historical record visualization module provides a timeline slider or calendar selection function, allowing operators to review equipment operating data from the past day, week, or even longer periods. Key events, such as health scores and fault alarm times, are highlighted with markers within the chart, facilitating analysis of the root cause and evolution of faults. Alarm information visualization and processing: When the fault diagnosis and assessment module for a device in the system determines that its health assessment level falls below a threshold or detects fault signs, an alert is immediately displayed through the visual human-computer interface. A pop-up window or alert bar appears at the top of the main interface, listing the faulty device, alarm level, alarm time, and specific diagnostic recommendations. Map-based location: If the device's location is visible on the plant layout, a flashing icon will appear on the alert layer, allowing users to click to view a detailed fault description and cause analysis. Interactive processing: Users can click "Confirm Action" to enter troubleshooting measures or comments, which are then saved to the historical database for subsequent audit and analysis. Health assessment reports and trend analysis: The visualization module integrates statistical analysis components, automatically generating weekly or monthly reports on each device's health score, fault index, and maintenance history. Users can view the following on the report page: Score change curves: This displays fluctuations in device health scores over time, allowing quick identification of device degradation trends; Maintenance and failure event distribution: For example, when alarms occur, when spare parts replacement or maintenance is performed, forming a closed-loop management system; Trend forecast charts: If advanced prediction algorithms are enabled, the system can generate prediction intervals and risk assessment curves to help develop more scientific maintenance plans.Mobile terminal synchronization and remote operation and maintenance: To facilitate on-site inspections or remote management, operation and maintenance personnel can log in to the visual human-computer interaction module through mobile APP, tablet computer or Web terminal: Instant push: If a motor fault alarm is triggered, the backend will immediately push relevant information through APP message, and inspection personnel can view the fault details on site; remote confirmation and dispatch: The "work order dispatch" function of the mobile terminal allows the operation and maintenance supervisor to directly issue maintenance tasks to designated personnel after seeing the alarm and check the progress at any time.

[0122] Preferably, the asynchronous motor precision spot inspection and diagnostic device comprises:

[0123] The local diagnosis unit is installed inside the motor control cabinet and is used to collect the three-phase voltage and current signals of the motor and perform real-time fault feature extraction;

[0124] an asynchronous motor precision inspection communication module, configured to transmit the monitoring data and processing results of the local diagnosis unit to the data storage module;

[0125] Asynchronous motor precision inspection local alarm unit, used to generate local sound and light alarms when signs of motor failure are detected;

[0126] The local 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 supply voltage fluctuation conditions, the local diagnosis unit identifies the motor stator winding inter-turn fault, rotor bar broken fault and bearing fault by using the negative sequence apparent impedance filter value and stator current modulus spectrum method.

[0127] In some embodiments, an asynchronous motor precision inspection and diagnostic device is used for condition monitoring and fault prediction of key auxiliary motors in power plants (such as induced draft fans, forced draft fans, and feedwater pumps). This device primarily consists of an on-site diagnostic unit, an asynchronous motor precision inspection communication module, and an asynchronous motor precision inspection local alarm unit. It combines signals from the secondary sides of voltage transformers (PTs) and current transformers (CTs) to enable high-speed acquisition and detailed analysis of the motor's three-phase voltage and current. Details are as follows: Installation and signal acquisition of the on-site diagnostic unit: Installation location: Place the on-site diagnostic unit in a spare area of the motor control cabinet. To facilitate on-site maintenance and repair, its housing features an industrial-grade dustproof and electromagnetic interference-resistant design. Signal input: The diagnostic unit obtains the motor's three-phase voltage and current through secondary wiring of the voltage transformer and current transformer. If a 1A current transformer output is used on-site, a dedicated terminal block is provided on the secondary side to ensure safe and accurate measurement. Real-time Sampling: The high-resolution acquisition card within the diagnostic unit synchronously samples voltage and current at an adjustable sampling rate (typically 1-2 kHz), obtaining the instantaneous and effective values of each phase waveform and preserving complete fault characteristic frequency band information. Fault Feature Extraction and Negative-Sequence Apparent Impedance Filter Value Calculation: During each acquisition cycle, the local diagnostic unit first performs phasor decomposition on the three-phase voltage and current signals to obtain positive- and negative-sequence components. The negative-sequence apparent impedance signature is then extracted using a specific filter. If this value shows a significant increase or fluctuation relative to the baseline, a preliminary judgment is made regarding the possibility of a stator winding interturn fault. Stator Current Modulus Spectrum Analysis: The three-phase currents are subjected to a fast Fourier transform (FFT) or higher-resolution spectrum estimation to extract rotor fault characteristic components such as (1±2s)·f and record the harmonic amplitudes. A significant increase in amplitude indicates a broken rotor bar fault. Furthermore, for bearing fault scenarios, the diagnostic unit searches the current spectrum for characteristic frequencies of the bearing vibration-coupled current and determines their significance. Asynchronous Motor Precision Inspection Communication Module, Data Packaging and Upload: After the diagnostic 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 a comprehensive health index. This data is then transmitted to the asynchronous motor precision inspection communication module via 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 connection with a host computer or data storage module. To prevent network impact and external interference, key data fields can also be encrypted or CRC-checked for enhanced security and reliability. Asynchronous Motor Precision Inspection Local Alarm Unit, Sound and Visual Alarm Principle: When the local diagnostic unit determines that the fault index exceeds a threshold or the health score falls below the system's preset safety threshold, it outputs a trigger signal to the local alarm module. This module drives a buzzer and warning light mounted on the outside of the control cabinet to issue an audible and visual alarm, alerting personnel on duty.Alarm maintenance and reset: If the equipment condition returns to normal, the diagnostic unit will feed back the "fault cleared" signal to the alarm module, stopping the whistle or flashing; at the same time, the alarm clearing information will be pushed to the upper system to support the background to automatically record the start and end time of this alarm. Under different load and voltage fluctuation conditions, this embodiment can perform early perception and classification diagnosis of stator winding inter-turn faults, rotor bar broken faults, and bearing faults through comprehensive analysis of the negative sequence apparent impedance filter value, stator current modulus spectrum, and harmonic characteristics. If a minor abnormality is detected, the system will issue a warning in the form of a local sound and light alarm and a pop-up window on the remote platform, allowing operation and maintenance personnel to arrange targeted inspections or maintenance. If the abnormality is determined multiple times in a row or a serious fault index is suddenly detected, a high-level alarm will be triggered immediately and a shutdown and maintenance recommendation will be made to avoid further damage.

[0128] In some embodiments, obtaining operating parameter and environmental parameter data of the power generation equipment includes:

[0129] Temperature sensor, attached to the high-voltage switch contacts or busbar connector, with a sampling frequency of not less than 1kHz;

[0130] The 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 motor bearing status;

[0131] Current transformer, collecting three-phase current waveform data;

[0132] Voltage transformer, collects three-phase voltage waveform data.

[0133] In some embodiments, to comprehensively capture key operating and environmental parameters of power generation equipment, multiple sensors or transformers are deployed at high-voltage switch contacts, busbar connectors, motor bodies, and locations within the control cabinet. These sensors collaborate to collect and transmit temperature, vibration, voltage, and current signals. The main implementation steps are as follows: Temperature Sensor Installation Location: Attach the temperature sensor near the high-voltage switch contacts or busbar connectors. The sensor's back is secured with high-temperature-resistant double-sided tape or a binding to ensure stable contact and prevent loosening. Sampling Frequency: The temperature sensor is set to a sampling frequency of no less than 1kHz. This is particularly critical when detecting sudden overheating or localized temperature rise, allowing for timely capture of temperature transients. Data Upload: After local signal conditioning, the sensor signal is connected to a multi-source data access module via wired or wireless means. Temperature data is then transmitted to a 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 utilizes an integrated sensor that combines a microelectromechanical system (MEMS) gyroscope with an acoustic emission probe to simultaneously detect mechanical vibration and microcrack acoustic wave signals. Installation and Calibration: The sensor is mounted on the motor bearing end cap or base using a magnetic mount or bolts. The sensor and measurement point are kept fixed relative to each other, and calibration testing is performed before the system goes online. Fault Monitoring: When bearing surface spalling or raceway microcracks occur, the acoustic emission probe captures high-frequency sound waves, and the MEMS gyroscope detects the vibration's micro-amplitude and spectral characteristics, providing critical raw data for subsequent fault diagnosis and assessment modules. Current transformers collect three-phase current waveform data. CT Specification Selection: Select appropriate current transformers (CTs) based on motor power and rated current range. The standard secondary output is 1A. Installation and Secondary Side Safety: The CT primary side passes through the motor power cable or busbar, while the secondary side connects to a multi-source data access module or asynchronous motor precision inspection and diagnostic device via a dedicated terminal block. To address safety and interference concerns, the secondary circuit is equipped with a short-circuit terminal and shield grounding. Real-time Waveform Acquisition: During motor operation, the CT output current waveform, which varies with load, is sampled at high speed and used for subsequent analysis, such as negative-sequence apparent impedance calculation and rotor bar broken spectrum detection. The voltage transformer collects three-phase voltage waveform data. Wiring form: A voltage transformer (PT) is configured in the distribution circuit to convert high voltage of several thousand volts into a secondary signal of several hundred 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 on-site diagnostic unit or data storage module for joint collection to obtain the waveform of each phase voltage changing with time, including information such as grid harmonics and unbalanced components. Linkage analysis: When the voltage transformer and current transformer signals are sampled synchronously, vector calculation can be used to obtain comprehensive indicators such as the power, power factor, negative sequence current and negative sequence voltage of the power generation equipment for real-time monitoring of the motor operating condition.

[0134] Preferably, the data storage module includes:

[0135] Real-time database, used to store the most recent or short-term monitoring data and support high-frequency queries;

[0136] Historical database, used to store long-term monitoring data in time series to support trend analysis, fault tracing and health modeling;

[0137] The data interface management unit is used to receive the original data from the multi-source data access module and write it into the database after standardization.

[0138] In some embodiments, the data storage module is deployed on a server in the power plant data center, aiming to simultaneously meet the needs for rapid query of real-time, high-frequency data and the need for long-term retrospective analysis of massive historical data. Its 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 the three-phase current waveform of an asynchronous motor, gas concentration and temperature in transformer oil, etc.), it will quickly write it to the real-time database through the network interface. Because real-time databases are typically based on memory or high-performance timing structures, they 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-computer interaction interface needs to obtain the current status of the equipment, the system directly retrieves the latest sampled values or data from the past few minutes / seconds from the real-time database, enabling instant reporting and charting of high-frequency dynamic changes. This allows for rapid problem location and preliminary analysis when an anomaly is discovered. Long-term data maintenance and segmented archiving in the historical database: Data retained in the real-time database typically only covers the most recent data or a short time window (such as half an hour or several hours). To prevent overload of the real-time layer, the module archives data that has reached or exceeded its retention period to the historical database at a pre-set interval (for example, every 15 minutes or one hour). Time Series Retrieval and Multidimensional Analysis: The historical database, based on a time series format or distributed storage structure, indexes monitoring data by device number, timestamp, and data type. Maintenance personnel or data scientists can use APIs or specialized query statements to conduct in-depth searches based on time range, device ID, or fault tag, supporting applications such as trend analysis, fault tracing, model training, and health modeling. Elastic Capacity Expansion: As a power plant connects to more monitoring devices or the data volume continues to grow, the historical database can be flexibly expanded by adding nodes or storage shards to ensure uncompromised read and write efficiency. Standardized processing, protocol parsing, and format conversion in the data interface management unit: Data input from the multi-source data access module may contain messages in Modbus TCP, OPC UA, or proprietary protocol formats. The data interface management unit parses, maps fields, and converts data types. For example, it converts the sensor JSON field into a unified key-value pair structure and uses timestamps as indexes. 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 that the information finally written to the database is of high quality. Before writing to the historical database, additional tags (such as "fault event" and "alarm confirmation") may be attached to facilitate subsequent retrieval. Access permission management: When different users or application systems call data, the interface management unit decides whether to release it or perform partial desensitization based on the role or security policy to prevent important parameters from being unauthorizedly obtained or modified.

[0139] In some embodiments, the multi-source data access module implements data integration by:

[0140] Time series alignment is applied to battery monitoring data;

[0141] Spectral analysis technology is used to analyze the dissolved gas data in transformer oil;

[0142] High-frequency signal filtering and feature extraction are used for partial discharge data.

[0143] In some embodiments, the multi-source data access module needs to integrate multiple monitoring systems of different types, sampling frequencies, and data formats. To ensure accurate data alignment and usability, the following specific measures are implemented: Time series alignment of battery monitoring data. Data source: The TLI-X8 online battery monitoring system periodically outputs key parameters such as battery voltage, current, and cell internal resistance, and uploads them to the multi-source data access module via a gateway in JSON or CSV format. Timestamp parsing: Because the battery monitoring system and the multi-source data access module may use different clock sources or time accuracy, after receiving data, the access module first reads the timestamp of each record and converts it to a unified UTC format or a standard timeline based on the factory time zone. Sequence alignment: This embodiment aligns each battery data point on the timeline through interpolation or zero padding. For example, if this system reads battery data at 30-second intervals, while other monitoring devices have a higher data refresh rate, new time points that were not collected are filled in through interpolation to ensure that the timelines of each record are consistent when later correlated analysis or visualization with other monitoring data. Spectral analysis of dissolved gas data in transformer oil and raw data acquisition: The TRANSFIX™ online monitoring system periodically outputs raw data on oil gas concentration, trace water content, and photoacoustic spectroscopy. The multi-source data access module directly captures these concentration values and spectral curves using a compatible communication protocol (such as Modbus or OPC UA). Spectral analysis and processing: The access module includes a pre-built spectral analysis algorithm to separate and extract features from the absorption peaks of various fault gases in the oil (such as key gases like CO, C₂H₂, and CH₄), automatically identifying whether the fault gas content exceeds safety thresholds. Data standardization: Analysis results and concentration values are formatted and stored in the same fields, or only key indicators such as dissolved gas content and spectral peak positions are extracted and written to a database. This allows transformer oil monitoring data to be displayed synchronously with other equipment parameters or used for comprehensive diagnostics. High-frequency signal filtering and feature extraction of partial discharge data and data sampling: Online generator partial discharge monitoring devices (such as the W-PD6) often acquire discharge pulse waveforms at a high sampling rate and, through pre-processing, obtain the partial discharge intensity (PDI) or high-frequency signal envelope. High-frequency signal filtering: The multi-source data access module performs denoising and bandpass filtering on the high-frequency pulse envelopes from the generator partial discharge online monitoring device, eliminating power frequency and low-frequency interference to extract the pulse components truly related to the discharge. Feature extraction: Based on the filtering results, the module uses a feature extraction algorithm to calculate key parameters such as pulse amplitude, pulse interval, and accumulated energy. If abnormal values (such as a continuous increase in pulse intensity) persist, they will be marked as "suspected discharge faults" at this stage and transmitted to the fault diagnosis and assessment module or directly trigger an early warning.

[0144] Preferably, the fault diagnosis and evaluation module includes:

[0145] A mother convolutional neural network model is used to perform feature extraction and calculation on input vectors consisting 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.

[0146] The data input to the mother convolutional neural network model also includes feature data calculated and obtained by the fault diagnosis algorithm library.

[0147] In some embodiments, the fault diagnosis and assessment module uses a mother convolutional neural network (hereinafter referred to as the mother CNN) to perform deep feature extraction on multi-source data about the motor and its environment. Combined with the feature data generated by the fault diagnosis algorithm library, this module achieves highly sensitive online detection of multiple fault types. Its system structure and operating principles can be described as follows: Input data organization and preprocessing, multi-dimensional input vector: The input to the mother CNN consists of (voltage, current, negative-sequence impedance, harmonic components, temperature, motor load conditions) and "feature data calculated by the fault diagnosis algorithm library." Time window segmentation: Real-time or historically stored monitoring data is segmented into fixed-length time windows (e.g., 1 second or 5 seconds), and each channel signal is normalized or standardized within the window to ensure that the input features are within a similar numerical range, facilitating convolution operations. Multi-channel stitching: 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.), this embodiment stitches all channels into a multi-channel tensor in the "channel dimension" with the format of batch size, number of channels, and time step to adapt to the CNN input layer format.

[0148] The structure of the mother convolutional neural network model: First convolutional layer (Conv1): The convolution kernel size is set to 3×1 or 5×1 to capture local features along the time series axis; the number of channels is set to 16 or 32 filters, and the activation function can be ReLU or a modified activation function, making the model more sensitive to instantaneous changes in voltage and current. Second convolutional layer (Conv2): After initially extracting the underlying features, the second convolution layer expands the receptive field and uses a larger convolution kernel (such as 5×1) to capture the changing patterns of fault harmonics over a longer time period; the number of channels is increased to 64 filters; batch normalization is used during training to improve network stability. Pooling and skip connections: After each convolutional layer, the mother CNN can insert a max pooling or average pooling layer to reduce data dimensionality. Skip connections can also be set between layers to make the network more likely to retain key detailed features and avoid gradient vanishing. The third convolutional layer (Conv3) further aggregates multi-channel information to deeply mine high-frequency harmonics, negative-sequence current fluctuations, and temperature-load coupling variations. This layer is followed by a Flatten operation, which expands the convolution output into a one-dimensional feature vector for integration in the fully connected layer. Fully connected layers: The flattened feature vector is input into one or two fully connected layers for comprehensive judgment. This layer can output one or more fault scores (such as stator winding interturn fault score, rotor bar broken score, and bearing fault score), which can also be combined into a comprehensive health score.

[0149] In this embodiment, the input to the parent CNN includes not only raw waveforms (voltage, current, temperature, load, etc.), but also feature data calculated by the fault diagnosis algorithm library, such as traditional diagnostic indicators like the negative-sequence current component and (1±2s)·f harmonic amplitude; spectral center frequency; and vibration signatures. These a priori features complement the local patterns extracted by CNN deep learning, improving the model's discrimination and response speed for various fault types. During the training and operation process, labeled normal or faulty samples from historical data are segmented by time window and fed into the parent CNN. Iterative training is performed using stochastic gradient descent (SGD), Adam, or other optimization algorithms, with accuracy, error, and overfitting monitored on a validation set. If a physical constraint neural network or other sub-models are introduced, these can also be trained jointly or in stages during this phase. Online operation phase: Once real-time data is read in, the multi-source data access module and data storage module perform preprocessing. The fault diagnosis and assessment module feeds the newly arrived data blocks (including prior features) into the parent CNN for forward calculation to obtain a fault score or health status. If the score falls below the safety threshold or there is a significant surge in fault characteristics, the early warning module is triggered to alert the operation and maintenance personnel.

[0150] Through deep convolutional learning of multi-channel data and prior features, the mother CNN can extract relatively stable and sensitive feature components even in the presence of noise and fluctuations in asynchronous motors and their environmental parameters. When historical environmental conditions differ significantly from current load conditions, the CNN can maintain good fault identification robustness under multiple operating conditions through hierarchical feature fusion and skip connections. Combined with traditional feature data derived from a fault diagnosis algorithm library, the network's initial convergence speed and accuracy are further enhanced, reducing reliance on large-scale, purely data-driven models and forming a dual guarantee of "deep learning + domain prior knowledge." Through this embodiment, the mother convolutional neural network model can more effectively mine potential correlations and fault signal patterns from multi-dimensional data (such as electrical, temperature, harmonics, and negative-sequence impedance) of power generation equipment, significantly improving diagnostic sensitivity and interference resistance, and providing more accurate technical support for early fault detection and subsequent operation and maintenance decisions for critical equipment.

[0151] Preferably, the fault diagnosis algorithm library is based on the following method:

[0152] The stator winding inter-turn fault adopts negative sequence current component analysis to generate first analysis data;

[0153] The rotor bar broken fault is detected by detecting the (1±2s)*f frequency component in the stator current spectrum to generate detection data; where s represents the slip rate of the asynchronous motor and f is the fundamental frequency of the power supply;

[0154] The bearing fault generates the second analysis data through the joint analysis of the vibration signal in time and frequency domains.

[0155] In some embodiments, the fault diagnosis algorithm library pre-includes the analysis methods and feature extraction processes required for major motor fault types, providing a priori feature information to the fault diagnosis and evaluation module based on real-time or offline motor monitoring data. Specifically, the following describes the following: Stator winding interturn fault analysis: Data source: Three-phase current signals collected at high speed in the asynchronous motor precision inspection and diagnostic device are uploaded to the fault diagnosis algorithm library via the multi-source data access module. Negative-sequence current component calculation: The algorithm library first performs phase decomposition on the three-phase current to separate the positive-sequence, negative-sequence, and zero-sequence components, focusing on the amplitude and phase angle of the negative-sequence component. Fault feature identification: If the negative-sequence current component increases significantly relative to the baseline value or shows a persistent trend of exceeding the limit, a potential risk of stator winding interturn short circuit is determined, and first analysis data (including negative-sequence amplitude, rate of change, and fault index) is generated. The analysis results are then packaged and transmitted to the fault diagnosis and evaluation module for input into the parent convolutional neural network model or for directly triggering an early warning. Broken rotor bar fault analysis and frequency component detection: Utilizing Fast Fourier Transform (FFT) or other high-resolution spectral estimation methods, the three-phase stator current waveform is subjected to spectral analysis, focusing particularly on the harmonic amplitudes at characteristic frequencies such as (1±2s)*f. Slip calculation: Under actual motor operating conditions, the slip s can be estimated from parameters such as load torque and speed. The algorithm library substitutes this into (1±2s)*f to precisely locate the fault harmonic.

[0156] Fault indication output: If the harmonic amplitude exceeds the normal benchmark and continues to increase, it is considered a sign of a broken rotor 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 and evaluation module or data storage module to further confirm the fault level in combination with other dimensional features. Joint analysis of bearing faults in time and frequency domains, vibration and acoustic emission data: Vibration sensors or acoustic emission probes installed in the motor bearings will capture mechanical vibration waveforms and microcrack noise signals at a higher sampling rate. Time-frequency domain fusion: The algorithm library uses short-time Fourier transform (STFT) or wavelet transform to jointly analyze the vibration signal in the time domain and frequency domain, and extract characteristic values such as envelope spectrum, pulse interval, energy entropy, etc.; in addition, the peak value and main frequency band of the acoustic emission signal can be statistically analyzed. Second analysis data generation: If a typical bearing fault characteristic frequency peak or high-amplitude pulse is detected in the vibration envelope spectrum, the algorithm library will determine the risk of bearing surface spalling or raceway cracks and output secondary analysis data (bearing fault frequency, pulse intensity, cumulative damage index, etc.). After integration with other electrical parameters, it provides key input for the parent CNN model or other sub-models.

[0157] Through this implementation, the fault diagnosis algorithm library specifically extracts core feature components when detecting three common motor fault types (stator winding interturn failure, rotor bar breakage, and bearing damage). The results are then summarized into a unified feature data format for subsequent comprehensive judgment by the parent convolutional neural network model or other fault diagnosis and assessment algorithms. This combination of "algorithm library prior knowledge + deep learning" significantly improves the detection sensitivity and judgment accuracy of various motor faults.

[0158] 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 respectively. 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 to prompt that the equipment may have early hidden dangers, and it is recommended to arrange manual inspections, calibrate sensors or enter the key monitoring queue in the near future; when the score is lower than 0.5, it is regarded as a "normal state", and the system generates a prompt message, 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 threshold judgment logic, and links with the operation and maintenance strategy library to generate corresponding processing suggestions, realizing intelligent response to different health states.

[0159] 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.

[0160] Preferably, the digital twin module supports the following functions:

[0161] Dynamic visualization of equipment configuration;

[0162] Infrared thermal imaging and real-time data superposition display;

[0163] Matching historical fault case libraries and recommending maintenance strategies.

[0164] In some implementations, to enable operations and maintenance personnel to more intuitively and comprehensively understand the real-time operating status and potential fault evolution of power generation equipment, a digital twin module has been added to the visual human-computer interaction module. This module also expands upon this with features such as dynamic visualization of equipment configuration, infrared thermal imaging overlay display, and matching with a historical fault case library. The main implementation process is as follows: Construction and initialization of the digital twin model. 3D modeling: Based on the actual structure of the power plant equipment, a virtual model is generated using CAD drawings, 3D scanning, or specialized modeling software (such as SolidWorks and 3ds Max). This includes the asynchronous motor, transformer, generator body, busbar, and key connecting components. Model parameter initialization: The virtual model is aligned with the actual equipment, combining the equipment nameplate information, rated parameters, dimensions, and spatial coordinates stored in the data storage module. This ensures a high degree of consistency in the size and position of each component within the digital twin environment. Communication interface: The digital twin module exchanges data with the multi-source data access module or the fault diagnosis and assessment module through a visual human-computer interaction interface, enabling real-time mapping of the equipment's current operating parameters (such as temperature, current, and voltage). Dynamic visualization of device configuration and dynamic model drive: On the digital twin platform, each device object (such as a motor or transformer) is equipped with several visual components (rotors, bearings, terminals, etc.). When actual operating parameters change, the system adjusts the model's color, speed animation, or transparency accordingly. For example, if the negative-sequence apparent impedance of a motor increases, the winding color displayed on the model will gradually change to indicate this. Interactive configuration management: Operations and maintenance personnel can select a device in the interface, expand the component tree to view its logical relationships and real-time values, and dynamically add or remove sensor layers. This provides a comprehensive overview of the device's configuration, operating data, and monitoring point layout. Infrared thermal imaging and real-time data overlay display and 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 into the digital twin model according to a calibrated coordinate system. Visual fusion: When the system receives a new infrared image, it instantly generates a color spectrum distribution map, which is overlaid with the virtual 3D model to display temperature hotspots on the device surface. If the local temperature of certain electrical contacts is too high, it will be prominently marked on the model (for example, by flashing red or by an animated hot zone diffusion). Data linkage: Any high-temperature area found in thermal imaging will also automatically match the corresponding sensor temperature record (such as wireless temperature measurement module data) to facilitate comparison and verification of whether the safety threshold has been exceeded. Historical fault case library matching and recommended maintenance strategies, case library retrieval: When the digital twin module identifies a certain fault sign (such as abnormal motor bearing temperature or a sharp increase in partial discharge signals), it will perform similarity matching based on the system's built-in historical fault case library (including recorded fault occurrence time, fault mode, repair plan, etc.).Recommended maintenance strategies: If the system retrieves similar historical cases, and the handling measures in those cases successfully prevented the accident or reduced losses, the digital twin module will pop up a "Maintenance Recommendations" window on the visual interface, displaying information such as the possible root cause of the fault, priority inspection areas, estimated maintenance hours, and spare parts requirements; operations and maintenance personnel can quickly formulate or adjust maintenance plans accordingly. Evolutionary process simulation: Digital twins can also simulate the spread of faults within the equipment structure. For example, if the rolling elements of a bearing continue to wear, the model will display the evolution trend of vibration energy or temperature zones frame by frame to help assess the serious consequences that may result from delayed faults.

[0165] Preferably, a data interface for the SCADA system is also included to achieve seamless integration of partial discharge intensity (PDI) signals and temperature data. The SCADA system's data interface utilizes standard industrial communication protocols (such as Modbus TCP, IEC 61850, or OPC UA), enabling data connection via a communication gateway deployed between the monitoring master station and the generator partial discharge online monitoring device. The monitoring master station, located at the core of the enterprise LAN, serves as both a data center and an interaction portal for operations and maintenance personnel, providing a summary interface for higher-level systems (industry cloud platforms). The generator partial discharge online monitoring device can upload real-time collected PDI signals and simultaneously measured high-voltage contact temperature data in the form of periodic messages. The SCADA system, by configuring a unified data point table and variable mapping, parses this monitoring data and integrates it into a unified scheduling platform. In terms of system architecture, the multi-source data access module serves as an intermediate layer, preprocessing the PDI and temperature values (such as denoising, completion, and timestamp alignment) before synchronously writing them to the SCADA database. To ensure real-time and consistent integration, the system incorporates a data caching and synchronization mechanism. This ensures that PDI and temperature data are displayed without delay on the SCADA interface and triggers alarms or linkage policies, enabling seamless integration and unified monitoring. The data caching and synchronization mechanism operates as follows: The cache-synchronization process utilizes a "sequence number + cursor + ACK" mechanism: The field gateway first writes each monitoring message into a circular memory buffer, automatically appending a millisecond timestamp and an auto-incrementing sequence number. Every 500ms, the synchronization thread batches unsent segments, packages them, and pushes them to the central server via MQTT or OPC UA. Upon receipt, the server returns an ACK based on the sequence number. The gateway then slides the cursor based on this ACK to dequeue the acknowledged segment. If no ACK is received within 5 seconds, the segment enters the retransmission queue and records the retry count. The cursor is written to local flash memory every minute, allowing transmission to resume at the most recent sequence number after a power outage. When the link switches between LoRa and 5G, the synchronization thread reads the cursor and continues transmission 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 fill it in 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.

[0166] Preferably, the asynchronous motor precision spot check diagnosis includes the following steps:

[0167] Real-time collection of motor stator current, voltage and vibration signals;

[0168] Extract negative sequence impedance characteristics and spectrum characteristics;

[0169] Determine the risk level of inter-turn short circuit, rotor bar breakage, and bearing failure through sub-machine learning models;

[0170] Generate maintenance recommendations and push them to user terminals.

[0171] In some embodiments, a precision inspection and diagnostic process for asynchronous motors is deployed within a power plant environment to address the daily operation and fault prediction needs of on-site asynchronous motors. The process specifically includes the following steps: Real-time acquisition of motor stator current, voltage, and vibration signals. Signal sources: An on-site diagnostic unit installed in the motor control cabinet collects three-phase current and voltage from the secondary sides of current transformers (CTs) and voltage transformers (PTs). Vibration sensors are deployed at bearing locations to monitor mechanical vibration waveforms in real time. Sampling control: The system determines the sampling frequency (e.g., 1kHz to 2kHz) based on load size and process requirements. The collected waveform data is digitized, timestamped, and uploaded to a precision inspection and diagnostic device or a multi-source data access module. Negative-sequence impedance characteristics and spectral features are extracted. Negative-sequence impedance calculation: The three-phase voltage and current vectors are decomposed into positive and negative sequences, extracting the negative-sequence voltage and current components and calculating the negative-sequence apparent impedance. An abnormal increase in negative-sequence impedance or fluctuations exceeding a safety threshold indicates a risk of interturn faults in the motor's stator windings. Spectral feature acquisition: Fast Fourier transform (FFT) or other high-resolution spectral estimation is performed on the current and vibration waveforms, focusing on characteristic harmonics such as (1±2s)·f (where s is the slip and f is the fundamental frequency), as well as peak or envelope spectral anomalies caused by common bearing fault frequencies. These spectral features serve as a key basis for fault identification. A sub-machine learning model is used to determine the risk level of interturn short circuits, broken rotor bars, and bearing faults. Multimodal input: Negative sequence impedance values, spectral features, and vibration signal parameters (such as RMS acceleration and energy in the main vibration frequency band) are input into the sub-machine learning model. Incorporating environmental data (such as temperature and load variations) and historical maintenance records provides a more comprehensive assessment of the motor's health. Intelligent analysis: The sub-machine learning model can utilize methods such as deep neural networks, support vector machines, or physically constrained neural networks (PC-DNNs) to classify or grade fault type and severity. A fault index or risk score is output for stator winding interturn short circuits, broken rotor bars, and bearing wear, maintaining a certain confidence interval to improve the accuracy and interpretability of the judgment. Generate maintenance recommendations and push them to the user terminal. Maintenance recommendation generation: If a fault risk score exceeds a preset threshold or the health score significantly declines, the sub-model automatically generates maintenance or inspection recommendations based on a pre-defined maintenance strategy library. These recommendations include information such as the suspected fault location, risk persistence, and recommended downtime for inspection or replacement of wearing parts. Push and closed-loop management: The system instantly communicates these maintenance recommendations to on-site operations and management via a visual human-computer interaction module or mobile app. Operations and maintenance personnel can provide feedback to the system regarding final inspection results and spare parts replacement status, forming a complete closed-loop management and fault evolution profile.Through the above steps, this embodiment realizes the real-time collection and intelligent analysis of the motor's electrical parameters and vibration information, and can quickly warn and make maintenance decisions in the early stages of stator winding interturn, rotor bar breakage and bearing failure, significantly reducing the probability of overhaul and the risk of unplanned downtime, and improving the safety and reliability of power generation equipment.

[0172] In some embodiments, the sub-machine learning model typically utilizes a multi-module system integrated in series and parallel, encompassing components such as a physically constrained neural network (PC-DNN), a dynamic graph attention support vector machine (DGAT-SVM), and a cross-domain incremental learning unit. The core concept is to integrate traditional machine learning classification and regression techniques with motor physics equations, dynamic graph topology, and incremental learning, maintaining high accuracy and robustness even under complex operating conditions. The following is a brief description of each sub-module: The PC-DNN (physically constrained neural network) embeds physical priors, such as the electromagnetic-mechanical coupling equations of asynchronous motors (e.g., Maxwell's equations and Hertzian contact theory), into the deep neural network structure. A physical loss function or differentiable operator is used to enforce that the network output meets certain device mechanism constraints, reducing reliance on large amounts of labeled data and preventing predictions that violate physical laws. The DGAT-SVM (dynamic graph attention support vector machine) models device relationships as a dynamic graph structure, where nodes represent individual devices or key subsystems and edges represent fault correlations or energy couplings. An attention mechanism is introduced to focus on the most representative subset of edges or nodes. Support vector machines (SVMs) are then used to classify or regress node (device) failure risks. By dynamically updating edge weights, the model can adapt to changes in operating status and historical fault propagation information. Cross-domain incremental learning and uncertainty quantification, employing techniques such as Adversarial Domain Adaptation (ADA) and online knowledge distillation, address the distribution discrepancies between simulation data and measured field data, supporting rapid adaptive learning when new operating conditions or new equipment are online. Monte Carlo dropout or fuzzy membership functions are used to quantify output confidence, providing operators with a credible interval or degree of deviation for fault judgments.

[0173] In some embodiments, multimodal input fusion is implemented, acquiring raw data such as motor voltage, current, vibration, and temperature from a multi-source data access module, along with characteristic values from a fault diagnosis algorithm library (e.g., negative sequence impedance, (1±2s)·f, and other harmonic indicators). If environmental parameters (e.g., load, humidity, ambient temperature) or historical fault case matching information are also included, these are incorporated into the input vector and, after preprocessing, passed to the sub-machine learning model. Combining physical constraints with deep learning, the PC-DNN component first performs multi-layer perception and convolution operations on the multimodal input to extract potential fault features. Physical constraint units are also added to the network, such as discretizing Maxwell's equations into differentiable operators to correct for the coupling between the stator electromagnetic distribution and the rotor fault. In the bearing fault branch, the roller-raceway stress distribution from Hertzian contact theory is used as prior knowledge and injected into the convolution kernel weights or regularization terms, forcing the network output to conform to physical laws. A dynamic graph attention mechanism: For scenarios involving multiple devices or coupled systems (e.g., a motor failure may affect other devices), the DGAT-SVM component abstracts these devices / systems into a dynamic graph. Node features can include vibration, current harmonic distortion, temperature gradients, and other factors, while edge weights are updated based on historical fault propagation probabilities or electrical connectivity. This attention mechanism automatically assesses the importance of each node and edge, focusing on critical paths that may trigger linked failures. Support vector machines are then used to classify or regress node-level or system-level risk. Cross-domain incremental learning and uncertainty assessment: When new operating conditions, new devices, or new failure modes emerge, the cross-domain incremental learning module uses Adversarial Domain Adaptation (ADA) technology to reduce the discrepancy between real-world data distribution and existing simulation data. Simultaneously, online knowledge distillation integrates new knowledge into existing models, enabling them to adapt to changing scenarios without requiring large-scale retraining. The uncertainty quantification engine uses Monte Carlo dropout and fuzzy membership at the model output layer or at the DGAT-SVM decision edge to provide confidence intervals and fuzzy metrics for failure probabilities or health scores, helping operations and maintenance personnel assess the reliability of diagnostic results. Output fault risk levels and maintenance recommendations. After the above sub-model process, each motor or other equipment receives one or more fault risk scores (such as stator fault score, rotor fault score, bearing fault score), as well as a comprehensive health index. If the risk score exceeds the threshold, an early warning is triggered, and the maintenance strategy library is called to generate a more specific maintenance plan or spare parts list. At the same time, the diagnostic details and confidence level are pushed to the visual human-computer interaction module.

[0174] In some embodiments, the following is the processing flow of the sub-machine learning model: data acquisition: obtain real-time / historical data + algorithm library prior features; feature preprocessing: normalization, sliding window segmentation or high- and low-frequency stratification; PC-DNN analysis: deep network + physical equation constraints, output intermediate fault features; DGAT-SVM analysis (optional, for multi-device coupling scenarios): build a dynamic graph, focus on key nodes and edges, and SVM derives device or system-level failure risks; incremental learning and uncertainty quantification: if there are new working conditions or failure modes, perform adversarial domain adaptation or online distillation, and calculate the output credible interval; result release and maintenance strategy recommendation: if the fault score exceeds the limit, push warnings and maintenance plans; otherwise, continue to accumulate data for health monitoring. Fusion of physical mechanisms: PC-DNN avoids blind data-driven learning and improves generalization and credibility in small sample environments; Dynamic fault correlation: DGAT-SVM can identify coupled fault propagation paths between multiple devices, truly achieving system-level diagnosis; Continuous evolution: Cross-domain incremental learning can quickly adapt to changes in operating conditions or the addition of new equipment without the need for large-scale offline retraining; Reliable output: Uncertainty quantification provides a risk buffer for operation and maintenance decisions, helping managers understand diagnostic confidence and reduce losses caused by misjudgment.

[0175] In some embodiments, the sub-machine learning model employs a hybrid architecture combining a deep neural network (DNN) and a support vector machine (SVM). This hybrid architecture leverages the automatic feature extraction capabilities of deep learning with the robust performance of SVM for small-sample, high-dimensional classification problems. This hybrid model is targeted at fault diagnosis scenarios involving asynchronous motors and other critical power generation equipment. The specific implementation steps are as follows: Deep neural network (DNN) feature extraction. Input layer: This layer aggregates the feature vectors generated by the multi-source data access module or fault diagnosis algorithm library (such as motor three-phase voltage and current, ambient temperature, vibration signal statistics, negative-sequence impedance, harmonic amplitude, etc.) into a unified input matrix or tensor format. Hidden layer design: A DNN typically consists of several fully connected hidden layers, each equipped with a nonlinear activation function such as ReLU or LeakyReLU. Batch normalization can also be added to stabilize the training process. Feature Dimensionality Reduction and Fusion: If the input data is too high-dimensional, dimension reduction layers (such as fully connected layers with dropout) or autoencoder structures can be placed after some hidden layers to compress and fuse multimodal information, remove redundant features, and extract the key information in high-dimensional space. High-Order Representation Output: After the last hidden layer, the DNN outputs an intermediate feature vector, often 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, Receiving DNN Output: The high-order feature vector extracted by the DNN is input into the SVM classifier or regressor. Because the DNN has already fully learned the features of the noise and nonlinearities in the original data, the SVM can perform efficient classification or regression in a relatively compact feature space. Kernel Function and Parameter Selection: For the identification of multiple motor fault types, RBF (Radial Basis Function) kernels or polynomial kernels can be used. If the sample set contains multiple fault categories, a multi-classification strategy (such as One-vs-Rest or One-vs-One) is used to train and fuse the discrimination results between each fault type and the healthy state. Hyperparameter training: Utilizing historical fault data and normal operating data for training and validation, grid search or genetic algorithms are used to optimize the SVM penalty coefficient C and kernel function parameter γ, ensuring stable, high-precision classification boundaries even under small sample sizes or complex nonlinear conditions. The overall hybrid process training phase: Pre-training the DNN to initially acquire the ability to extract motor state features. While freezing or fine-tuning the DNN weights, the intermediate feature vectors are then fed into the SVM classifier / regressor for continued training, ultimately achieving fault category identification or health score regression.During the online inference phase, multidimensional sensor data from motors or other devices is acquired in real time. DNN forward calculations output a deep feature vector. This deep feature vector is then fed into an SVM to determine the fault type and output an anomaly level or health score. If a high-risk fault is identified or the score falls significantly below a threshold, the early warning module is triggered and maintenance recommendations are pushed to the operator. Advantages and application value: Efficient feature extraction: DNNs can automatically learn highly distinguishable nonlinear features from raw data (voltage, current waveforms, vibration time-frequency analysis values, etc.), complementing complex patterns that are difficult to capture with purely manual features. Robust discrimination: SVMs use kernel functions to construct optimal margin hyperplanes in high-dimensional space, maintaining good discrimination in the presence of noise and local outliers while reducing the risk of overfitting. Flexible combination: In actual applications, the number of DNN layers, number of neurons, and SVM kernel function form can be adjusted based on hardware resources and fault type requirements, making this hybrid architecture suitable for both large-scale historical data and small sample sizes or sudden faults in the field.

[0176] Preferably, the maintenance suggestion includes:

[0177] Fault location accuracy is not less than 95%;

[0178] Maintenance time window recommendation;

[0179] Spare parts inventory matching information.

[0180] In some embodiments, a multi-source fusion diagnostic mechanism collaborates with a physically constrained neural network model to achieve a fault location accuracy of at least 95%. Specifically, the system utilizes three-phase voltage and current waveforms collected by an asynchronous motor precision inspection and diagnostic device, combined with multimodal data such as partial discharge, temperature, and vibration, and feeds these into multiple fault branches of a sub-machine learning model. Through feature cross-enhancement and comparison with a fault label library, high-precision fault location identification is achieved. For maintenance time window recommendations, the system combines equipment operating time, current health score trends, and fault evolution rate curves to predict the fault risk growth range within the next 3 to 7 days and generate the optimal recommended downtime maintenance 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 and uses 0-1 integer programming to solve the minimum power generation loss ΔP to obtain a set of possible downtime dates. The 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. The one with the largest f is selected as the "optimal downtime maintenance window" (i.e., the optimal recommended downtime maintenance time period). Spare parts inventory matching information is linked to the enterprise resource management system (ERP) or spare parts management database to retrieve the current available inventory, model matching and procurement cycle in real time, automatically compare the currently determined faulty component type, and output the optimal spare parts replacement plan. By comparing the BOM material code of the faulty part with the inventory code, if the same model is out of stock, the "alternative part similarity" calculation is performed according to ISO 15243 / ISO 281 parameters (size ±5%, load ±10%, material consistency), allowing maintenance personnel to make quick decisions, thereby realizing intelligent and accurate maintenance suggestion display.

[0181] The above-mentioned feature cross-enhancement and comparison with the fault label library are achieved through the following method. First, a multimodal feature matrix is generated for the original sequences of three-phase voltage, current, vibration, temperature rise, and PDI. Based on the original sequences, 42-dimensional features such as time-domain statistics, FFT / envelope spectrum, negative-sequence impedance, and temperature rise gradient are extracted. Cross-enhancement: The mutual information of each feature is calculated based on the Gradient Boosted Decision Tree. Feature pairs with mutual information greater than 0.15 are selected for Hadamard multiplication or quotient ratio to form a 180-dimensional cross-feature. The fault label vector library: 1500 manually confirmed inter-turn, broken bar, and bearing samples are collected offline. These enhanced features are clustered after dimensionality reduction using the Unified Mapping (UMAP) method to obtain nine label center vectors. Online identification: The cosine similarity between the real-time features and each label center vector is calculated. If the cosine similarity is ≥0.95, the corresponding part is located. The confidence interval is combined to output a positioning accuracy of ≥95%.

[0182] Preferably, the sub-machine learning model adopts a multimodal meta-learning enhanced architecture, including:

[0183] The physical constraint neural network (PC-DNN) embeds the motor electromagnetic-mechanical coupling equations as a physical constraint layer in a deep neural network. This is achieved through the following methods:

[0184] In the stator winding fault diagnosis branch, the Maxwell equations are discretized into differentiable operators, forcing the network output to satisfy the electromagnetic field distribution law;

[0185] In the bearing fault diagnosis branch, the stress distribution calculated by Hertz contact theory is injected into the convolution kernel weight initialization as prior knowledge.

[0186] In some embodiments, the sub-machine learning model utilizes a multimodal meta-learning enhancement architecture, including a "Physically Constrained Neural Network (PC-DNN)" module. This module embeds the motor's electromagnetic-mechanical coupling equations as physical priors within the deep neural network to improve the generalization of fault diagnosis in small-sample, highly complex operating conditions. The model structure and operation can be described as follows: The overall multimodal meta-learning framework includes input data: Current, voltage, vibration, temperature, and other multi-source information collected by the system from asynchronous motor precision inspection and diagnostic devices and other sensors, along with characteristic indicators provided by the fault diagnosis algorithm library (such as negative-sequence current components, harmonic amplitudes, and vibration envelope characteristics), which together constitute the model's multimodal input. Meta-learning enhancement: After initial training, the model can continuously adapt to new operating conditions or equipment data through incremental learning or adversarial domain adaptation (ADA), avoiding repeated training from scratch while ensuring continuous evolution in complex scenarios. The structure of the physically constrained neural network (PC-DNN). Deep network body: PC-DNN consists of several layers of convolutional / fully connected layers, which are specifically designed for high-dimensional multi-source data for asynchronous motor fault detection. Branches are set in the network structure to correspond to different fault types (stator winding fault, rotor fault, bearing fault, etc.); 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 the form of a differentiable operator, so that the network output must meet or be close to the corresponding physical equation constraints when updating the weights. Stator winding fault diagnosis branch: The Maxwell equations are discretized into differentiable operators. Discretization of the Maxwell equations involves discretizing the electromagnetic field distribution of the motor stator winding using finite element or differential methods, resulting in a set of differential equations that approximately describe the relationship between the stator flux linkage, magnetic flux density, and current. Embedding: During training, the PC-DNN adds a "physical constraint loss" to the loss function to measure the difference between the network output (such as the stator flux distribution prediction) and the results calculated using the discretized Maxwell equations. The network output is forced to conform to the laws of electromagnetic field distribution. When the network prediction deviates too far from the physical truth, the physical constraint loss increases accordingly. Backpropagation adjusts the weights and forces the network to more accurately conform to the actual electromagnetic field distribution. This ensures that the diagnostic results for stator winding interturn faults remain physically reasonable and highly accurate, even with limited training samples.Bearing fault diagnosis branch: The stress distribution calculated by Hertz contact theory is injected as prior knowledge into the convolution kernel weight initialization. Hertz contact theory: By calculating the contact pressure distribution and stress peak position between the rolling element and the bearing raceway, the impact pattern on the vibration signal or acoustic emission signal in the early stage of bearing fatigue or surface spalling can be inferred; Convolution kernel weight initialization: The convolution branch responsible for processing bearing vibration or acoustic emission data in PC-DNN, its convolution kernel will be initialized with reference to the spatial distribution law of Hertz contact stress at the initial or pre-training stage of the network; Fault sensitivity improvement: Since the initial weight of the convolution kernel already contains the characteristic structure of the bearing contact stress, the network is more likely to learn the key signal patterns of spalling cracks or abnormal wear when facing real vibration data, accelerate the convergence speed and improve the sensitivity to bearing faults. Model training and inference process: During the training phase, the PC-DNN is initially trained using historical fault data and normal operating data. During training, the "physical constraint loss" is used to evaluate the match between the network output and Maxwell's equations, while retaining the advantages of the convolution kernel initialized from Hertzian contact theory. If a multimodal meta-learning enhancement mechanism is added, it can also quickly adapt to new equipment or new failure modes through incremental learning. During the online inference phase, when the system receives real-time sensor data (current, voltage, temperature, vibration, etc.), each PC-DNN fault branch outputs a fault index or health score. If the stator winding fault branch detects abnormal negative sequence impedance or unreasonable magnetic flux distribution, it issues a turn-to-turn short circuit risk warning. If the bearing fault branch identifies abnormal vibration in a high-stress area, it determines a bearing wear or spalling fault and issues maintenance recommendations. Adaptability to small sample scenarios: When it is difficult to obtain a large amount of complete fault data on site, "network + physical equations" can force the model to follow 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 level of magnetic flux distribution, and the bearing fault branch can be traced back to stress distribution theory, making it easy for operation and maintenance personnel to understand the basis of the model; accelerated convergence: through the physical prior initialization method of the convolution kernel, not only the training cycle is shortened, but also the network learns more targeted feature extraction patterns. Therefore, it can be seen that the use of multimodal meta-learning enhanced architecture and the embedding of 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 small sample environments, but also ensure that the model's judgment results on motor faults are more consistent with physical mechanisms, comprehensively improving the credibility and sensitivity of fault diagnosis.

[0187] In some embodiments, the sub-machine learning model adopts a multimodal meta-learning enhanced architecture, further comprising:

[0188] The Dynamic Graph Attention Support Vector Machine (DGAT-SVM) constructs a dynamic graph structure based on the device operation topology relationship, where:

[0189] Node characteristics include real-time current harmonic distortion rate, vibration spectrum entropy value and temperature gradient;

[0190] Edge weights are dynamically updated based on the electrical connection strength between devices and the historical fault propagation probability;

[0191] The attention mechanism is used to focus on key fault-related paths and output device-level and system-level risk scores.

[0192] In some embodiments, as Figure 2 As shown, a four-tiered architecture ("site-enterprise-industry-country") enables large-scale health monitoring and remote diagnosis. The field layer includes various on-site devices (such as main motors, fans, and pumps), which transmit raw data such as current, voltage, vibration, partial discharge, and temperature to the enterprise LAN via local data collection terminals. These terminals typically access switches via Gigabit Ethernet or LoRa / 5G, maintaining sub-second communication with the backend. The enterprise LAN layer includes a data server responsible for real-time and historical database management, storing all monitoring data. An enterprise communication proxy server is responsible for protocol conversion, data encryption, and external publishing. The enterprise diagnostic and early warning workstation deploys a parent CNN and child machine learning models to comprehensively score and classify field data. Mobile and fixed users (mobile phones, tablets, and on-call PCs) access the workstation via the intranet or VPN client to view digital twins, alarm logs, and maintenance recommendations. Perimeter security and industry network access include a triple security layer of gateways (see figure): routers and firewalls deployed at enterprise egress. The gateway performs address translation, the router implements QoS and dynamic routing, and the firewall implements whitelisting, intrusion detection, and port isolation. After a security audit, selected health data, alarm summaries, and energy efficiency metrics are allowed into the industry LAN. The industry LAN layer includes an industry diagnostic and early warning platform, which aggregates data uploaded by multiple enterprises and utilizes big data models for horizontal comparison and group trend prediction. On-duty experts can access the risk rankings, spare parts shortages, and energy efficiency of each member unit via desktop or mobile devices. The platform also generates industry-level action recommendations for critical alarms and sends them back to each enterprise. The National Diagnostic and Early Warning Center periodically sends summary information, such as lists of high-risk equipment and abnormal carbon emission trends, to the national center via a dedicated VPN. The National Diagnostic and Early Warning Center utilizes machine learning models with a larger sample size to conduct macro-risk assessments and provide policy-level guidance. Dispatch instructions or early warning notifications are then issued to the industry platform via a dedicated line, creating a vertically closed loop. Data flow and security control include the use of "end-pipe-cloud" three-stage encryption for the entire link: TLS+AES is used from the site to the enterprise intranet; IPsec is used from the enterprise to the industry network; VPN tunnel and national secret algorithm are used from the industry network to the national center. Multi-level identity authentication ensures that data is complete, traceable and tamper-proof during cross-domain transmission. Figure 2The topology shown in the figure realizes hierarchical data aggregation, intelligent analysis and remote collaborative processing from on-site equipment to the national platform, which not only meets the needs of rapid early warning within the enterprise, but also takes into account industry horizontal benchmarking and national macro-regulation. The system architecture is clear and the logic is complete.

[0193] In some embodiments, information from various on-site devices is collected and transmitted via enterprise and industry LANs. A sub-machine learning model employs a multimodal meta-learning-enhanced architecture. To enable more detailed coordinated fault analysis of the interconnected system of multiple devices within the plant (such as generators, asynchronous motors, and transformers), a "Dynamic Graph Attention Support Vector Machine (DGAT-SVM)" module is introduced. The operational process is outlined as follows: A dynamic graph structure is constructed, with device nodes representing key equipment within the power plant (such as asynchronous motors, transformers, and generators). Each node possesses several dynamic characteristics, including current harmonic distortion, vibration spectrum entropy, and temperature gradient. Edge definition: If two devices are significantly electrically or mechanically coupled (e.g., a motor and transformer on the same busbar segment, or a mechanical coupling drive), an edge is added to the graph. Initially, edge weights are set based on factors such as the strength of the electrical connection or the physical distance between the devices. Dynamic updating of node features and edge weights. Node features: In actual operation, the current harmonic distortion rate, vibration spectrum entropy, and temperature gradient of each device continuously evolve due to load changes, ambient temperature fluctuations, or fault symptoms. The DGAT-SVM periodically captures this feature data and updates the properties of each node in the graph. Adaptive edge weighting: If historical data indicates that a motor failure often causes anomalies in another device sharing the bus (higher probability of fault propagation), the edge weight is increased; otherwise, it is decreased. This allows for system-level identification of "active edges" and "low-risk edges" for fault propagation. The attention mechanism focuses on key fault-related paths. Attention scoring: When traversing the dynamic graph, the DGAT-SVM calculates the risk of each node (device) and the fault correlation between it and its neighboring nodes. The attention mechanism scores connections between devices based on edge weights, node feature similarity, and known fault history, highlighting paths most likely to form "fault chains" or linkage risks. Local subgraph mining: The attention mechanism focuses in-depth analysis on high-scoring edges or nodes. This helps quickly locate the most vulnerable nodes and coupling loops in scenarios with a large number of equipment and complex fault relationships, reducing interference from irrelevant information. Support vector machine (SVM) scoring output, equipment-level risk: For focused subgraphs or important nodes, DGAT-SVM inputs node characteristics (such as real-time current harmonic distortion, vibration spectrum eigenvalues, and temperature gradients) into an SVM classification / regression model to produce a risk score or health score for each node. For suspected faulty nodes, the SVM can provide a classification result (e.g., "bearing fault," "stator winding fault," etc.) or a failure probability. System-level risk: If the attention mechanism detects a strong coupled fault chain between multiple equipment nodes, DGAT-SVM calculates a system-level risk score, indicating the probability or severity of the associated faults that the entire plant or unit may face.This score can be used by the dispatch center or management to take timely intervention measures (such as adjusting the operating mode, pre-activating standby units, etc.). Fault warning and classification: After the DGAT-SVM derives the device-level and system-level risk scores, if the scores exceed the threshold or increase significantly, the warning module is triggered to indicate to the operation and maintenance personnel the equipment nodes that need the most attention or maintenance, as well as the possible transmission paths; 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 score and SVM classifier parameters to make the model more accurate in identifying future fault correlation propagation. Through the above process, this embodiment uses the dynamic graph attention support vector machine (DGAT-SVM) under the multimodal meta-learning enhanced architecture to effectively identify the risk of inter-device linkage failures: it can not only provide a fault judgment for a single device, but also evaluate the overall fault propagation trend of the system, helping the operation and maintenance and dispatching departments to better control the health of power generation equipment, thereby reducing the incidence of linkage failures and improving the reliability of safe operation.

[0194] In some embodiments, the sub-machine learning model adopts a multimodal meta-learning enhanced architecture, further comprising:

[0195] Cross-domain incremental learning module, including:

[0196] The simulation-measurement data alignment unit uses adversarial domain adaptation (ADA) technology to eliminate feature distribution deviations between the simulation model and the real device.

[0197] The online knowledge distillation pipeline encodes expert diagnosis rules in the historical fault case library into lightweight decision trees and outputs them for consistency constraints.

[0198] In some embodiments, the sub-machine learning model further integrates a cross-domain incremental learning module under the multimodal meta-learning enhanced architecture, aiming to address the challenges of "differences between the simulation environment and the actual field environment" and "the continuous emergence of new fault modes or diagnostic knowledge during field operation." This module contains two key units: a simulation-measurement data alignment unit and an online knowledge distillation pipeline, and the operation process is as follows. In the simulation-measurement data alignment unit, in the power generation environment, fault data is scarce and diversely distributed, and it may not be possible to collect enough measured samples for each fault mode; simulation platforms (such as finite element simulation and motor fault simulation) can generate a large amount of virtual fault data, but their data distribution is often significantly different from that of real equipment, such as noise level, environmental disturbance, load randomness, etc. Adversarial Domain Adaptation (ADA) technology applies the principle of domain adaptation: through adversarial neural networks or autoencoders, the feature distributions of the "simulation domain" and the "measurement domain" are made to tend to be consistent in high-dimensional space, reducing the problem of partial fitting of the model to the simulation data; the specific process: the simulation data and a small amount of measured data are input into a domain discriminator at the same time; the main network (generator / encoder) continuously learns how to map the simulation features to a representation space that is closer to the measured feature distribution; the domain discriminator tries to distinguish whether the input is from simulation or measurement. When the discriminator has difficulty distinguishing, it means that the feature distribution alignment is good; model benefits: after completing the domain alignment, the simulation samples can produce results that are more consistent with the measured environment in the training of the sub-machine learning model. Even if the actual fault data collected is very limited, the simulation data can still be fully utilized to improve the robustness of the model.

[0199] The online knowledge distillation pipeline uses a historical fault case library and expert diagnostic rules. Power plants often accumulate years of operational and maintenance experience and fault cases, which contain expert-derived "diagnostic rules" or "decision trees." For example, if the motor stator negative sequence current exceeds a certain value and the temperature rise slope exceeds a certain threshold, it is likely a turn-to-turn fault. If the vibration envelope spectrum continuously increases within a specific frequency range, it indicates the risk of bearing spalling. These "artificial rules" can quickly locate common faults, but lack the flexibility to adapt to new fault modes. The lightweight decision tree encoding and distillation process involves simplifying numerous expert rules into one or more small decision trees, logically branching based on key measurement points and thresholds, and consolidating the rule set with fewer nodes. Online knowledge distillation: When a sub-machine learning model (such as PC-DNN or DGAT-SVM) outputs fault risk, the distillation pipeline also captures the lightweight decision tree's judgment results. If there is a significant deviation from the model inference results, consistency constraints are used during training or incremental learning to adjust the model weights to ensure consistency with the expert rules for common fault types. Advantages: The distilled model can not only retain the expert rules' fast and stable judgment of mature fault scenarios, but also capture new patterns and complex signal features that are not explicitly covered by the expert rules through deep learning.

[0200] Data collection 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 that is closer to the measured domain under adversarial domain adaptation, and then sent to the sub-model together with the measured samples for training or incremental learning. Online incremental update: When new fault modes, equipment modifications, or working condition switching occur on site, the original model may not be able to make accurate judgments. The cross-domain incremental learning module will automatically call the simulation data for realignment and use a small number of samples collected on site for rapid fine-tuning. The online knowledge distillation pipeline also compares the existing expert decision tree. If the model output deviates from the mature rule branch, consistency correction is performed. Fault judgment and self-learning: The generated or updated model outputs the fault risk level of each motor or equipment. The operation and maintenance personnel test the model results. If the fault is successfully identified or the failure is determined, the new label information can be sent back to the database, and the model can continue to be iteratively adjusted. The collaborative approach of simulation and field measurement addresses the distribution discrepancy between simulation and field measurement through adversarial domain adaptation (ADA) technology, significantly improving the model's diagnostic reliability and generalization capabilities in real-world production environments and reducing over-reliance on long-term field fault data. Expert experience integration: The online knowledge distillation pipeline incorporates valuable manual decision trees into sub-models, accelerating the identification of common faults while ensuring that the deep learning model can continuously update as new fault patterns emerge, unconstrained by traditional rules. Real-time incremental learning allows the model to continuously make small adjustments as equipment operating conditions change, gradually adapting to new load environments, temperature ranges, or data characteristics after structural modifications, maintaining consistently high diagnostic accuracy. For small sample sizes, complex field conditions, or difficult-to-reproduce fault scenarios, the cross-domain incremental learning module not only significantly improves the model's adaptability but also ensures that existing expert knowledge is not erased, thus achieving the optimal combination of "human-machine collaboration" and "simulation-field integration."

[0201] Preferably, the sub-machine learning model adopts a multimodal meta-learning enhanced architecture, further comprising:

[0202] Uncertainty quantification engine, which assesses the confidence of diagnosis results by:

[0203] Apply Monte Carlo Dropout perturbation to the PC-DNN output and calculate the confidence interval of the failure probability distribution;

[0204] A fuzzy membership function is introduced into DGAT-SVM to quantify the gradual process of equipment status deviating from the normal threshold.

[0205] In some embodiments, the training data of the sub-machine learning model further includes:

[0206] A multi-physics coupled fault dataset based on finite element simulation covers combined fault scenarios such as rotor eccentricity and insulation aging. The training data for the sub-machine learning model is partially derived from the multi-physics coupled fault dataset generated by the simulation platform. This dataset is obtained by constructing a three-dimensional electromagnetic, thermal, and stress field model using motor finite element modeling tools such as ANSYS Maxwell. Taking the combined scenario of rotor eccentricity and insulation aging as an example, the system first establishes a structural geometric model of the asynchronous motor and sets operating parameters such as stator and rotor material parameters and air gap asymmetry. Dynamic eccentricity simulation is performed by varying the rotor position. Thermal aging factors such as decreased insulation thermal conductivity and increased thermal aging time are then added. The electromagnetic and temperature field distributions are jointly solved, and corresponding time-series data (such as stator leakage magnetic flux density distribution and insulation hotspot temperature) are extracted to form a labeled fault sample set. This data can cover abnormal scenarios that are difficult to replicate or collect under operating conditions, significantly enhancing the model's generalization capabilities. Furthermore, the system's full lifecycle carbon footprint data is collaboratively collected by the system's operation and management platform. Specifically, during the equipment's operation phase, the system continuously records its power output, power quality, and motor efficiency curves. This data is combined with on-site environmental parameters (such as temperature, humidity, and load conditions) to analyze efficiency degradation trends. The carbon metering module also accumulates carbon emissions (kgCO₂ / kWh) per unit of energy consumption during operation. This data, combined with estimated carbon emissions from equipment manufacturing, transportation, maintenance, and disassembly, forms a complete carbon footprint profile for the entire equipment lifecycle. During model training, this carbon footprint data, along with historical fault records and health score trends, is fed into a sub-model to learn the inherent correlation between energy efficiency degradation and failure modes, enhancing the model's capabilities in energy-saving state prediction and green O&M recommendations. Therefore, the system's training data is derived from a fusion of high-confidence simulation and full-process measured data. This not only addresses the problem of generating fault samples in low-sample scenarios, but also expands the intelligent model's understanding of the relationship between equipment performance, energy efficiency, and carbon emissions, providing data support for intelligent and green O&M of power generation equipment. Carbon footprint data from the entire equipment lifecycle is used to correlate failure modes with energy efficiency degradation.

[0207] In some embodiments, the sub-machine learning model utilizes a multimodal meta-learning-enhanced architecture and, to enhance the interpretability and reliability of equipment fault diagnosis results, includes an additional "uncertainty quantification engine" module. This module integrates Monte Carlo Dropout and fuzzy membership functions into the physically constrained neural network (PC-DNN) and dynamic graph attention support vector machine (DGAT-SVM), respectively, to comprehensively assess the credibility and asymptotic deviation of diagnostic results. The specific implementation process is as follows: Monte Carlo Dropout is applied to the PC-DNN. While PC-DNN (physically constrained neural network) typically disables Dropout by default during inference (it only serves to prevent overfitting during training), Monte Carlo Dropout retains Dropout by randomly setting it to zero during inference. The output distribution is then statistically analyzed after multiple forward passes. When the same data is input, each forward pass produces slight variations due to the random nature of Dropout, resulting in a set of failure probability or health score samples and an estimated mean and standard deviation.

[0208] A dropout layer is added after the key convolutional or fully connected layer of the PC-DNN, and the activation units are randomly masked during the inference phase. For a time window or a single sampling of data, multiple (e.g., 20 or 50) forward inferences are performed, recording the failure probability or score for each time. All inference results are statistically analyzed to obtain the mean (as the final diagnostic value) and the standard deviation or interval range (as a measure of uncertainty). A confidence interval is output. If the standard deviation is small, it indicates that the PC-DNN has a high degree of confidence in the fault judgment. If the standard deviation or interval is large, it indicates that the model may have uncertainty about the current operating conditions and requires further manual investigation or more data support. In the visual interface, the fault score can be presented as an interval strip, allowing operation and maintenance personnel to quickly judge the "confidence" of the model output. The introduction of fuzzy membership functions in DGAT-SVM measures node state deviation. DGAT-SVM constructs a dynamic graph structure between devices. Node characteristics such as current harmonic distortion rate, temperature gradient, and vibration energy may fluctuate at any time. To reflect the possibility of a "gradual transition" rather than a "one-size-fits-all" boundary between device states from "normal" to "abnormal," this embodiment introduces a fuzzy membership function during SVM judgment.

[0209] 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 gradually. Quantification of the gradual process, through the attention mechanism, focus on the nodes and edges with the most signs of faults, and calculate 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 early maintenance or status tracking based on this. The "system-level risk score" output by 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, electrical signals are obtained by extracting three-phase current and voltage from the secondary side of the control cabinet's CT / PT. These are coupled via dedicated current-voltage sensors to ensure safe isolation of the primary busbar. For mechanical signals, acceleration-type vibration sensors (with optional acoustic emission probes) are placed on the motor bearings or base to capture mechanical fault characteristics such as bearing spalling and rotor eccentricity. The signal acquisition system's acquisition module, which includes a multi-channel synchronous A / D converter and isolation conditioning circuitry, synchronously samples current, voltage, and vibration waveforms at a set frequency (1-5 kHz) and timestamps each frame. Electrical quantities are first converted to amplitude / phase quantities; vibration quantities undergo analog preamplification and digital filtering before being packaged into a feature packet and sent to a host computer (including monitoring, diagnosis, and early warning software). This monitoring, diagnosis, and early warning software runs on an industrial computer embedded in the control cabinet or an edge computing node. It uses a parent convolutional neural network model to generate a comprehensive health score. It then uses a child machine learning model to perform branching judgments on stator winding interturn, 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 range, 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 back-end management system diagnostic software uploads the health score, fault type, and vibration-electrical characteristics to the data center via Ethernet or LoRa / 5G gateway. The back-end 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 failures; all sensor channels meet the 2.5 kV isolation and IEC 61000-4-4 immunity requirements to ensure data and personnel safety in high-voltage environments. Through Figure 3The structure shown in the figure above demonstrates that this invention completes signal acquisition, edge diagnosis, and cloud management within a closed loop. The current / voltage and mechanical vibration information streams converge at the acquisition layer, undergo fusion analysis at the algorithm layer, and are then sent to the management layer for closed-loop maintenance. This solution avoids large-scale modifications to the existing control loop while enabling high-precision online monitoring of early-stage motor faults from multiple sources.

[0210] In some embodiments, the system also collects multiple signals, and multi-source data (voltage, current, vibration, temperature, etc.) are inferred in parallel or serially by PC-DNN and DGAT-SVM; PC-DNN outputs confidence intervals: performs multiple forward calculations of Monte Carlo Dropout to obtain the average diagnostic value and variance, which are used to construct the fault probability or health score interval; DGAT-SVM fuzzy discrimination: inputs the node (equipment) features into SVM for classification or regression, and calculates the fuzzy membership function at the same time, and outputs the degree of asymptotic deviation; comprehensive credibility: if the PC-DN interval is too large or the DGAT-SVM fuzzy value continues to grow, the uncertainty quantification engine will give a "warning" and recommend further manual inspection or model retraining; otherwise, the diagnosis result is relatively stable and reliable. Improve the explainability of fault diagnosis: Through confidence intervals and fuzzy membership, operation and maintenance personnel not only know whether the model determines "whether there is a fault" but can also evaluate the reliability of the judgment and the progressive magnitude 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 operation and maintenance decision-making: for situations near the critical value, if the fuzzy membership indicates "continuous deviation and aggravation", preventive maintenance is arranged in a timely manner; if the interval fluctuation is small and the μ value is extremely low, it can continue to operate safely, thereby improving equipment availability.

[0211] In some embodiments, the wireless communication module includes a heterogeneous communication gateway that supports dual-mode communication between the LoRa low-power wide area network (LPWAN) and the 5G network, and employs a dynamic routing algorithm based on device topology. The wireless communication module utilizes a heterogeneous communication gateway architecture, integrating LoRa and 5G communication modules. It implements dual-mode concurrent communication and intelligent switching through a unified network management interface. Specifically, the gateway hardware is equipped with independent LoRa communication chips (such as the Semtech SX127x series) and 5G communication modules, with scheduling and control performed by a central microprocessor. The software layer loads dual-channel drivers and runs protocol adaptation middleware via an embedded operating system (such as OpenWRT or embedded Linux), enabling access to both the LoRa private gateway and the public 5G core network, achieving dual connectivity, resource redundancy, and intelligent network selection. Regarding communication strategy, the system automatically selects the preferred link based on network quality assessment mechanisms (including link latency, packet loss rate, RSSI, and other indicators). For example, in underground power plants or areas with high electromagnetic interference, LoRa is preferred for transmission due to its strong anti-interference capabilities, wide coverage, and low power consumption. In scenarios where data uploads require high frequency, high bandwidth, and low latency (such as high-frequency partial discharge waveforms or AI model results), 5G channels are switched to ensure real-time and reliable transmission. Furthermore, dual-link automatic failover and data retransmission mechanisms are supported to ensure uninterrupted monitoring in the event of single-link communication anomalies. Regarding the dynamic routing algorithm based on device topology, this invention constructs a device network topology map based on the physical connection relationships and communication layer logical adjacencies between each field device, and uses an improved dynamic AODV protocol as the underlying routing mechanism. The gateway regularly broadcasts device status, link quality, and data transmission pressure information, dynamically generating the optimal path through weighted calculations. These weighting factors include physical distance, communication success rate, device level (such as whether it is a core node), and historical failure frequency. In scenarios where a large number of sensor nodes are deployed, this algorithm automatically forms a fault-tolerant multi-hop routing path and dynamically updates the routing table when devices are added or taken offline, maintaining network connectivity and efficiency.

[0212] Preferably, the activation function used by the mother convolutional neural network model is It is expressed as follows: ;

[0213] Activation function used by the neural network in the physical constraint sub-machine learning model It is expressed as follows: ;

[0214] in, 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, Indicates the real-time monitored frequency offset on the electrical side, that is, the difference from the nominal frequency of 50Hz; is the dynamic change of the environment side, that is, the normalized sum of temperature, wind speed and load changes; is the coupling coefficient; a larger value indicates greater sensitivity to changes in motor dynamic characteristics. The activation function of the parent convolutional neural network adds a "slip" adjustment to the conventional activation function, shifting the activation curve exponentially based on the motor slip value. As the load increases and the slip rises, the activation function automatically shifts, causing the network's input sensitivity range to change accordingly, enabling more accurate detection of stator winding fault signs. Slip is a critical operating parameter of asynchronous motors. Directly incorporating slip at the network activation level reduces reliance on large amounts of training data and better adapts to rapidly changing operating conditions. The physical constraint neural network activation function (Tanh-modified) of the child machine learning model adds a control factor to the input of the traditional hyperbolic tangent function. Its value depends on the sum of the "electrical-side frequency offset" and the "environmental-side dynamics." A coupling coefficient is also set to adjust the impact of external disturbances on the activation value. By comparing the power supply reference frequency with the real-time monitored frequency offset and then superimposing it with environmental variables such as temperature, wind speed, and load disturbances, the network can fully consider multiple external factors when activating the output. For example, when the power supply offset increases and the ambient temperature rises sharply, the activation function will enter the saturation value faster in the positive direction, helping the model to quickly identify potential faults related to this.

[0215] The mother convolutional neural network (CNN) performs nonlinear transformations after the convolution layer: After each convolution or batch normalization operation, the activation units perform a nonlinear mapping between the convolution output and the slip value. Before fully connected or pooling layers: Typically in the middle or late stages of the network, activation functions are applied immediately after the convolution or pooling layers to enhance the recognition of features such as stator current and negative-sequence impedance. By incorporating slip information, the network can more effectively distinguish signal fluctuations caused by load changes from true fault characteristics. The sub-machine learning model (physical constraint neural network) is deployed in the fault branch or fusion layer: Because this model extracts features for different branches, such as the motor stator windings, bearings, and rotor, activation functions are deployed at key nodes after the convolution / fully connected layers in 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 is applied again to ensure that the final output is sufficiently responsive to external disturbances under the current operating conditions. Dynamically adjust the neuron's sensitivity range. When the motor load or environmental disturbances are relatively stable, the activation function only undergoes a slight offset. However, under frequent start-stop or high-load conditions, the activation curve changes significantly, allowing the network to maintain high-resolution recognition of potential fault characteristics. Adapting to slip and environmental variables can reduce missed detections or misjudgments under extreme operating conditions. In line with physical equations, in the sub-network, after incorporating the motor's electromagnetic-mechanical coupling equations, the activation function simultaneously utilizes "power supply frequency offset + environmental variables" to achieve output variations that are more closely aligned with actual field conditions, avoiding predictions that contradict the actual mechanism. This model also reduces the network's reliance on large-scale training samples, maintaining excellent diagnostic performance even with small samples or under constantly changing operating conditions. Early fault detection is accelerated. Because the activation curve has adjustable gains for various external disturbances, if a motor exhibits characteristic anomalies (such as a slight negative-sequence impedance jump or frequency drift) during the initial onset of a fault, the network will more quickly detect the signal anomaly and accelerate early warning responses. Therefore, this embodiment introduces slip rate and electrical / environmental coupling into the activation functions of the parent convolutional neural network and the child machine learning model, respectively, so that the network can still accurately track fault characteristics and provide reliable health assessment results when processing complex scenarios such as high load changes, frequency offsets or temperature shocks.

[0216] The mother convolutional neural network model (mother CNN) performs large-scale deep convolution operations on massive amounts of sensor data (voltage, current, vibration, temperature, etc.), focusing on extracting multimodal global features. It also employs multi-layer fusion of overall fault characteristics under complex operating conditions to generate a comprehensive health score or global fault index. Using several convolutional, pooling, and fully connected layers, it extracts high-level semantic features (such as the motor's negative-sequence impedance trend, harmonic concentration, and temperature anomaly distribution) and ultimately outputs one or more judgment scores. In some embodiments, the outputs of certain intermediate layers of the mother CNN (deep feature embedding vectors) can be used as shared features for other algorithms. The resulting health score, typically a floating value ranging from 0.100 to 0.1, measures the current health status of the device. Alternatively, it outputs a comprehensive fault indicator (such as "failure probability 0.8" or "abnormality 0.6") suitable for quickly displaying the overall status of the device in a visual interface.

[0217] Sub-machine learning models (sub-models) provide more targeted, physically constrained, and detailed analysis of specific fault modes (such as stator winding interturn shorts, rotor bar breakage, and bearing spalling). They incorporate mechanisms such as physically constrained neural networks (PC-DNNs), dynamic graph attention support vector machines (DGAT-SVMs), and incremental learning modules to provide in-depth assessments of specific operating conditions or coupled faults. When targeting specific fault types, specialized prior knowledge such as the motor's electromagnetic-mechanical coupling equations, Hertzian contact stress distribution, or fault propagation topology is incorporated to enhance the network or algorithm's sensitivity to that fault mode. After processing the fault signature, the sub-model outputs a detailed fault risk level, evolutionary trend, or uncertainty quantification interval, enabling graded alerts and maintenance recommendations.

[0218] In some embodiments, each fault mode is assigned a score or level (e.g., "Inter-turn Failure Risk: High," "Rotor Bar Broken Probability: 30%," "Bearing Health Score: 78 / 100"). If incremental learning is integrated, the model may also output confidence intervals for the current data distribution differences, helping managers understand the reliability of the diagnostic results. The relationship between the two and the data flow, parallel or sequential: In some embodiments, the parent CNN and child models can be placed on the same server or deployed on different processing nodes. Their data flows are parallel and independent, without mutual exclusion or conflict. The parent CNN primarily performs global health analysis, while the child models focus on deep fault segmentation. The parent convolutional neural network model (parent CNN) and the child machine learning model can be flexibly configured in parallel or sequential mode within the system deployment architecture. In parallel mode, each runs as an independent thread or process on different computing cores or container environments on the backend server. Specifically, the parent CNN primarily extracts features and generates comprehensive health scores for large-scale raw data (e.g., continuous voltage and current waveforms). Its input data stream is connected to the data acquisition system via a cache or shared memory channel. After processing, the results are uploaded to the health assessment database. The child machine learning model uses structured feature data for refined identification of specific fault types (such as bearing or inter-turn shorts). It typically obtains cleansed and normalized fault feature sets, such as negative-sequence impedance, harmonic ratios, and temperature gradients, from a historical feature database or preprocessing module. The two models have different input sources and independent processing flows. In sequential invocation mode, the system's operating strategy allows the parent CNN to first perform a comprehensive score. If an abnormal trend is detected or the score exceeds a preset threshold, the child model is invoked to perform a secondary judgment. The parent CNN output then serves as a trigger, and the child model uses more granular data for targeted identification and hierarchical analysis. This architecture implements logical sequential orchestration through task scheduling middleware or a model invocation engine, avoiding data resource contention. To prevent data conflicts, the system design clearly decouples the model input buffer, output data structure, and scheduling priorities. Therefore, whether deployed on multi-core servers, Docker containers, or embedded edge computing nodes, the parent CNN and child models achieve structural decoupling, resource isolation, and independent scheduling, ensuring efficient diagnostic processes and stable system operation.

[0219] In some embodiments, the output of the parent CNN (e.g., comprehensive health score) and the fault risk level of the child model can be combined in a backend decision-making or weighted fusion. If the parent CNN detects a significant anomaly, the child model can be invoked for further investigation, similarly implementing a hierarchical diagnostic process. The final results are sent to the early warning module and the visual human-computer interaction interface, which can display the "comprehensive health" and "fault risk type / level" in the equipment overview view, respectively, allowing operations and maintenance personnel to determine specific maintenance strategies and timing.

[0220] The present invention provides an integrated power generation equipment health status monitoring system, which can achieve the following beneficial technical effects:

[0221] This invention organically integrates traditionally decentralized systems for online battery monitoring, intelligent data collection for collector rings, dissolved gas analysis in transformer oil, partial discharge monitoring, wireless temperature measurement, and precision inspection and diagnosis of asynchronous motors. Through a unified interface protocol and data management module, it enables centralized acquisition and visual management of multi-source heterogeneous data. This significantly reduces the workload for maintenance personnel who repeatedly switch between different systems, avoids information silos, and lays a solid foundation for subsequent system expansion and integration. By appending feature data calculated by a fault diagnosis algorithm library (such as negative-sequence current components and spectral characteristics) to the input layer of the parent convolutional neural network model, the invention directly incorporates the "prior knowledge" of various classical diagnostic methods, building on the raw data feature extraction through deep learning. This not only further enriches the model input dimension but also ensures the complementary advantages of deep networks and traditional diagnostic features, thereby enhancing both the reliability and sensitivity of fault detection and enabling more accurate and timely identification of early fault signs.

[0222] 2. The present invention utilizes a fault diagnosis and assessment module, relying on advanced algorithms such as a fusion of a parent convolutional neural network and a physical constraint sub-network, to rapidly identify early signs of motor stator winding interturn faults, broken rotor bars, and bearing faults under complex operating conditions. Furthermore, the system significantly improves the sensitivity and accuracy of fault identification by combining techniques such as negative-sequence apparent impedance, stator current modulus spectrum, and multimodal meta-learning. Once the health assessment level is detected to be below a threshold, the system automatically triggers an early warning and pushes maintenance recommendations to prevent further deterioration of the fault and unplanned downtime. The present invention uses a parent convolutional neural network model and a sub-machine learning model to predict power generation equipment faults, enabling early fault identification and significantly improving the accuracy and efficiency of equipment fault identification. The present invention rationally incorporates physical or operating condition information, such as motor slip and environmental interference coefficient, into the activation function of the parent convolutional neural network model, eliminating the input of the activation function as a simple linear transformation. This allows the network to adaptively adjust its output sensitivity in the face of changing operating conditions. This improved activation function not only enhances the robustness to complex factors such as electrical side imbalance and load fluctuation, but also effectively prevents gradient vanishing or gradient explosion, thereby shortening the convergence time while maintaining high-precision diagnosis.

[0223] 3. This invention adds a physical constraint neural network (PC-DNN) to the sub-machine learning model and embeds the motor's electromagnetic-mechanical coupling equations into the deep network. This allows the network to not only rely on data during training but also impose constraints on the network output through physical equations, forcing it to conform to the internal physical laws of the motor. This significantly reduces the reliance on large-scale annotated data, improves prediction accuracy and stability under complex operating conditions or when insufficient samples are available, and effectively prevents network outputs from being inconsistent with the actual physical mechanisms, greatly enhancing the reliability and interpretability of fault diagnosis. Introducing a dynamic graph attention support vector machine within a multimodal meta-learning architecture allows for the construction of a dynamic graph structure model based on the operating topology of devices, dynamically updating node and edge weights. Furthermore, the attention mechanism focuses on key fault correlation paths, enabling accurate identification of global fault propagation links. This combination significantly improves the ability to assess system-level fault risks. It can quickly extract key features in scenarios with complex fault correlations across multiple devices, reducing interference from irrelevant information, thereby significantly enhancing the overall system's fault prediction performance and real-time response capabilities.

[0224] The above is a detailed introduction to an integrated power generation equipment health status monitoring system. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core ideas of the present invention. At the same time, for those skilled in the art, based on the ideas and methods of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An integrated power generation equipment health status monitoring system, characterized in that: include: Multi-source data access module, used to access asynchronous motor precision inspection and diagnosis equipment, TLI-X8 battery online monitoring system, slip ring intelligent data acquisition system, transformer oil dissolved gas and trace water online monitoring system, generator partial discharge online monitoring device and wireless temperature measurement system, to obtain operating parameters and environmental parameter data of power generation equipment; A wireless communication module transmits the collected operating parameter and environmental parameter data to a data storage module; A data storage module, used for storing the operating parameters and environmental parameter data from the multi-source data access module; The fault diagnosis and assessment module is used to use the parent convolutional neural network model to perform global training on the acquired operating parameters and environmental parameter data of the power generation equipment to calculate and output the health assessment level, and uses the child machine learning model to calculate and judge specific fault modes and output the fault risk level; An early warning module is used to generate an alarm signal when the health assessment level or the fault risk level is lower than the corresponding set threshold; Visual human-computer interaction module, used to display the operating status, historical records, alarm information and health assessment level of power generation equipment on the platform interface or mobile terminal; The slip rate of the asynchronous motor is introduced into the activation function of the mother convolutional neural network model, and the activation function is immediately followed by the back end of the convolution layer or pooling layer of the mother convolutional neural network model; the dynamic change amount on the environmental side and the frequency offset on the electrical side monitored in real time are introduced into the activation function of the child machine learning model, and a coupling coefficient is set; the larger the value of the coupling coefficient, the more sensitive it is to the changes in the dynamic characteristics of the motor.

2. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The asynchronous motor precision spot inspection and diagnosis device comprises: The local diagnosis unit is installed inside the motor control cabinet and 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, configured to transmit the monitoring data and processing results of the local diagnosis unit to the data storage module; Asynchronous motor precision inspection local alarm unit, used to generate local sound and light alarms when signs of motor failure are detected; The local 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 supply voltage fluctuation conditions, the local diagnosis unit identifies the motor stator winding inter-turn fault, rotor bar broken fault and bearing fault by using the negative sequence apparent impedance filter value and stator current modulus spectrum method.

3. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The acquisition of operating parameters and environmental parameter data of the power generation equipment includes: Environmental parameters include temperature information collected by temperature sensors attached to high-voltage switch contacts or busbar connectors, with a sampling frequency of no less than 1 kHz; The operating parameters include mechanical vibration and micro-crack acoustic wave signals acquired by the vibration sensor, which integrates a MEMS gyroscope and an acoustic emission probe to monitor the motor bearing status; The operating parameters also include three-phase current waveform data collected by the current transformer and three-phase voltage waveform data collected by the voltage transformer.

4. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The data storage module includes: Real-time database, used to store the most recent or continuous monitoring data within a short period of time; Historical database, used to store long-term monitoring data in time series to support trend analysis, fault tracing and health modeling; The data interface management unit is used to receive the original data from the multi-source data access module and write it into the real-time database after standardization.

5. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The multi-source data access module achieves data integration in the following ways: Time series alignment is used on the battery monitoring data collected by the TLI-X8 battery online monitoring system; The spectral analysis technology is used to analyze the dissolved gas data in transformer oil collected by the online monitoring system for dissolved gas and trace water in transformer oil; High-frequency signal filtering and feature extraction are used to the partial discharge data collected by the generator partial discharge online monitoring device.

6. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The fault diagnosis and evaluation module includes: A mother convolutional neural network model is used to extract and calculate features from input vectors consisting of historical and real-time input voltage, current, negative sequence impedance, harmonics, temperature, and motor load conditions, and output a health assessment level to improve the sensitivity and robustness of fault diagnosis. The data input to the mother convolutional neural network model also includes feature data calculated and obtained by the fault diagnosis algorithm library.

7. The integrated power generation equipment health status monitoring system according to claim 6, characterized in that: The fault diagnosis algorithm library is based on the following methods: The stator winding inter-turn fault adopts negative sequence current component analysis to generate first analysis data; The rotor bar broken fault is detected by detecting the (1±2s)*f frequency component in the stator current spectrum to generate detection data; where s represents the slip rate of the asynchronous motor and f is the fundamental frequency of the power supply; The bearing fault generates the second analysis data through the joint analysis of the vibration signal in time and frequency domains.

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

9. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: 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.

10. The integrated power generation equipment health status monitoring system according to claim 9, characterized in that: The digital twin module supports the following functions: Dynamic visualization of equipment configuration; Infrared thermal imaging and real-time data superposition display; Matching historical fault case libraries and recommending maintenance strategies.

11. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: It also includes a data interface for the SCADA system to achieve seamless integration of partial discharge intensity (PDI) signals and temperature data.

12. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The precision inspection and diagnosis of asynchronous motors includes the following steps: Real-time collection of motor stator current, voltage and vibration signals; Extract negative sequence impedance characteristics and spectrum characteristics; Determine the risk level of specific fault modes using a sub-machine learning model; specific fault modes include turn-to-turn short circuits, broken rotor bars, and bearing failures; Generate maintenance recommendations and push them to user terminals.

13. The integrated power generation equipment health status monitoring system according to claim 12, characterized in that: The sub-machine learning model adopts a hybrid architecture of deep neural network and support vector machine; the parent 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 flow and calculation process remain independent without conflict.

14. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The visual human-computer interaction module also displays maintenance suggestions including: Fault location accuracy is not less than 95%; Maintenance time window recommendation; Spare parts inventory matching information.

15. The integrated power generation equipment health status monitoring system according to claim 12, characterized in that: The sub-machine learning model adopts a multimodal meta-learning enhanced architecture, including: The physical constraint neural network (PC-DNN) embeds the motor electromagnetic-mechanical coupling equations in a deep neural network as a physical constraint layer. This is achieved through the following methods: In the stator winding fault diagnosis branch, the Maxwell equations are discretized into differentiable operators, forcing the network output to satisfy the electromagnetic field distribution law; In the bearing fault diagnosis branch, the stress distribution calculated by Hertz contact theory is injected into the convolution kernel weight initialization as prior knowledge.

16. The integrated power generation equipment health status monitoring system according to claim 15, characterized in that: The sub-machine learning model also includes: Dynamic graph attention support vector machine DGAT-SVM builds a dynamic graph structure based on the device operation topology relationship, where: Node characteristics include real-time current harmonic distortion rate, vibration spectrum entropy value and temperature gradient; Edge weights are dynamically updated based on the electrical connection strength between devices and the historical fault propagation probability; The attention mechanism is used to focus on key fault-related paths and output device-level and system-level risk scores.

17. The integrated power generation equipment health status monitoring system according to claim 15, characterized in that: The sub-machine learning model also includes: Cross-domain incremental learning module, including: The simulation-measurement data alignment unit uses adversarial domain adaptation (ADA) technology to eliminate feature distribution deviations between the simulation model and the real device. The online knowledge distillation pipeline encodes expert diagnosis rules in the historical fault case library into lightweight decision trees and outputs them for consistency constraints.

18. The integrated power generation equipment health status monitoring system according to claim 15, characterized in that: The sub-machine learning model also includes: Uncertainty quantification engine, which assesses the confidence of diagnosis results by: Apply Monte Carlo Dropout perturbation to the PC-DNN output and calculate the confidence interval of the failure probability distribution; A fuzzy membership function is introduced into DGAT-SVM to quantify the gradual process of equipment status deviating from the normal threshold.

19. The integrated power generation equipment health status monitoring system according to claim 15, characterized in that: The training data of the sub-machine learning model further includes: A multi-physics coupled fault dataset based on finite element simulation, covering rotor eccentricity and insulation aging combined fault scenarios; The carbon footprint data of the equipment throughout its life cycle is used to correlate failure modes with energy efficiency degradation.

20. The integrated power generation equipment health status monitoring 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 device topology.

21. The integrated power generation equipment health status monitoring system according to claim 1, characterized in that: The activation function used by the mother convolutional neural network model is It is expressed as follows: ; Activation function used by the neural network in the physical constraint sub-machine learning model It is expressed as follows: ; in, 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, Indicates the real-time monitored frequency offset on the electrical side, that is, the difference from the nominal frequency of 50Hz; is the dynamic change of the environment side, that is, the normalized sum of temperature, wind speed and load changes; is the coupling coefficient. A larger value indicates greater sensitivity to changes in the motor's dynamic characteristics.

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