Intelligent fault diagnosis method, system and equipment for steam turbine generator unit and storage medium

Abstract

The invention provides an intelligent fault diagnosis method, system and device for a steam turbine generator unit, and a storage medium. The intelligent fault diagnosis method comprises the steps that running state data and external factor data of the steam turbine generator unit are acquired in real time through a multi-channel data acquisition system; performing multi-source feature extraction on the original data obtained after data preprocessing, and performing feature fusion on the extracted multi-source features; monitoring and fault diagnosis are conducted on the operation state of the steam turbine generator unit according to a pre-constructed intelligent fault diagnosis model, and the intelligent fault diagnosis model is a deep learning neural network model obtained through training based on the fused multi-source feature data. According to the method, the problems of data missing, data sparseness, data shortage and the like when a traditional deep learning method is applied to steam turbine generator unit fault detection are solved, high-precision monitoring and intelligent diagnosis and early warning of multi-component and multi-type coupling faults of a unit can be realized, and stable and reliable operation of the unit is effectively ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of steam turbine generator set fault diagnosis, and in particular relates to a steam turbine generator set intelligent fault diagnosis method, system, equipment and storage medium. Background Art

[0002] Steam turbine generator sets are core equipment in power systems, and their operational safety and stability are crucial to the entire system. Existing fault monitoring for steam turbine generator sets often suffers from missing or sparse data due to sensor failures, data upload delays, and other issues. Furthermore, the inconsistent dimensions and significant differences in importance of multi-source state parameters contribute to low fault monitoring accuracy, impacting the safe and stable operation of the units. Summary of the Invention

[0003] In view of this, the present application aims to propose an intelligent fault diagnosis method, system, equipment and storage medium for steam turbine generator sets, so as to solve the problem of data missing, sparse and scarce when traditional deep learning methods are applied to steam turbine generator set fault detection, resulting in inaccurate fault monitoring of steam turbine generator sets and affecting the stable operation of the units.

[0004] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0005] In a first aspect, the present application provides a method for intelligent fault diagnosis of a steam turbine generator set, comprising:

[0006] Acquire the operating status data and external factor data of the steam turbine generator set in real time through a multi-channel data acquisition system; wherein the operating status data includes the mechanical status data, drive status data and process status data of the main engine and auxiliary engine;

[0007] Extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features;

[0008] The operating status of the steam turbine generator set is monitored and fault diagnosis is performed based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on fused multi-source feature data.

[0009] In a second aspect, based on the same inventive concept, the present application also provides an intelligent fault diagnosis system for a steam turbine generator set, comprising:

[0010] a data acquisition module configured to acquire, in real time, operating status data and external factor data of the steam turbine generator set through a multi-channel data acquisition system; wherein the operating status data includes mechanical status data, drive status data, and process status data of the main engine and auxiliary engines;

[0011] The feature extraction module is configured to extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features;

[0012] The fault diagnosis module is configured to monitor the operating status and diagnose faults of the steam turbine generator set based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on the fused multi-source feature data.

[0013] In a third aspect, based on the same inventive concept, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0014] In a fourth aspect, based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0015] Compared with the prior art, the steam turbine generator set intelligent fault diagnosis method, system, device and storage medium described in this application have the following beneficial effects:

[0016] The intelligent fault diagnosis method, system, equipment and storage medium for steam turbine generator sets described in the present application collect multi-source feature data based on full-state parameter monitoring of the entire process flow of the monitored object, extract correlation features between different measuring points and different monitoring quantities, and realize high-precision detection of multi-component and multi-type coupled faults of the unit based on the constructed intelligent fault diagnosis model, ultimately achieving closed-loop control from monitoring to maintenance, and improving equipment reliability and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0018] Figure 1 This is a flow chart of an intelligent fault diagnosis method for a steam turbine generator set according to an embodiment of the present application;

[0019] Figure 2 This is a flow chart of an intelligent fault diagnosis system for a steam turbine generator set according to an embodiment of the present application;

[0020] Figure 3 This is a schematic diagram of the hardware structure of the electronic device described in an embodiment of the present application.

[0021] Description of reference numerals:

[0022] 11-data acquisition module; 12-feature extraction module; 13-fault diagnosis module; 1010-processor; 1020-memory; 1030-input / output interface; 1040-communication interface; 1050-bus. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0026] See also Figure 1 As shown, this embodiment provides an intelligent fault diagnosis method for a steam turbine generator set, which specifically includes the following steps:

[0027] Step S101: Acquire the operating status data and external factor data of the steam turbine generator set in real time through a multi-channel data acquisition system; wherein the operating status data includes the mechanical status data, drive status data and process status data of the main engine and auxiliary engine.

[0028] Specifically, in this embodiment, the steam turbine generator set includes eight main engines, namely six 350 MW main engines and two 600 MW main engines, and six auxiliary engines (a total of 12 feedwater pump turbines, encompassing major auxiliary equipment such as feedwater pumps, condensate pumps, circulating pumps, fans, and compressors). The vibration signals, phase signals, and shaft displacement signals of the main and auxiliary engines are output from the vibration signal buffer of the TSI system (steam turbine safety monitoring system). These signals are hard-wired into a data acquisition system via an interface module to ensure the safety of the TSI system. For other auxiliary engines and other equipment not yet connected to the online data acquisition system, data is collected via a multi-channel portable data acquisition and analysis instrument and connected to the intelligent platform.

[0029] The process signals (including temperature, flow, valve position, pressure, and power) are collected through the DCSAO module outputting 4-20mA signals and connected to the process signal acquisition module of the data collector, thus realizing hard signal access and ensuring the safety of the system.

[0030] The MD4008 intelligent data collector used in this solution can work on the front-end system. It is available in two models: 4 modules and 8 modules. Both have independent main control modules, complete module classification, and highly targeted signal processing. It can be flexibly configured according to the signal type. A high-speed communication bus is used between the module and the main control module, and the maximum communication rate can reach 40Gbps.

[0031] The MD4008 data collector modules can monitor and diagnose the operating status of equipment by real-time collecting, calculating, analyzing, and storing buffered signals from monitoring instruments / transmitters, as well as output signals from vibration sensors with direct voltage output (such as ICP accelerometers and eddy current displacement sensors), photoelectric / eddy current / magnetoelectric / speed sensors, and other sensors. The collected data can be transmitted to a host computer or central server via a variety of methods, including wired networks (network ports and optical ports) and 4G. It supports custom protocols and the MODBUS standard protocol, enabling communication with data from other user systems.

[0032] The collector can complete more than 80% of the data processing load, greatly reducing the workload of the system server and significantly improving the smoothness of system operation.

[0033] The data sampling strategy implemented by this collector is:

[0034] (1) For every 10 sets of static data collected, one set of dynamic data is collected. The sampling frequency is generally 32×rotation frequency, a total of 32 revolutions are collected, 1024 points of data are obtained, the spectrum resolution is 400 lines, and the fundamental frequency resolution is 1 / 32×rotation frequency;

[0035] The default setting for dynamic data acquisition is: 128 points / turn × 16 turns, that is:

[0036] ①The maximum analysis frequency is 50×rotation frequency;

[0037] ② Spectral resolution 400 lines;

[0038] ③ Green resolution 1 / 16×rotation frequency.

[0039] (2) Data is collected during the start-up and shutdown process in the form of △rpm (which indicates the speed difference between sampling intervals). The minimum speed should not be lower than 200rpm, where rpm indicates the speed per minute and the maximum speed is the rated maximum continuous operating speed of the unit. Within this speed range, 320 sets of static data and 32 sets of dynamic data are collected. Start-up and shutdown data need to be specially marked. When collecting dynamic data during the start-up and shutdown process, the number of collection cycles should not be too many (due to the large speed change rate, the more cycles, the greater the error). It is advisable to collect 8 cycles, and the sampling frequency is increased to 128×speed frequency;

[0040] (3) When an alarm event occurs, 50 sets of dynamic data and 500 sets of static data before and after the first alarm event are collected and "frozen" in a "first-in-first-out" data "black box". The black box data must be specially marked; the black box mode is a sampling frequency of 32× the rotation speed frequency, and a sampling length of 32 cycles;

[0041] (4) Collect a set of data at approximately 200 rpm and store it as "initial deflection" data for compensation in subsequent signal analysis. The method for collecting initial deflection data is exactly the same as that for collecting data in steady state (using synchronous full cycle acquisition, the sampling frequency and sampling length are the same);

[0042] 5. When a new unit is tested or tested after maintenance, one set of dynamic data (including 10 sets of static data) is stored as "initial state" data for subsequent comparative analysis;

[0043] (6) The capacity of the edge-side embedded database can be set according to actual needs. Generally, if 8-channel input is used, the embedded database memory capacity should be no less than 16GB (subject to actual calculation);

[0044] ⑺ The speed increase curve of the unit and the performance curve of the compressor can be stored in the database as benchmark data, which is convenient for monitoring the start-up and shutdown processes and when the unit is adjusting the load.

[0045] Step S102: extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features.

[0046] Specifically, in this embodiment, after the raw data is acquired, the data is subjected to three-level data processing and cleaning, the purpose of which is to clean the noise (for vibration, temperature and other data, the noise is removed by low-pass and high-pass filters to retain useful signals), fill in missing values (interpolation methods are used to fill missing sensor data, such as linear interpolation, Lagrange interpolation, etc.), and normalize different data scales (due to different data dimensions and unit differences, the data needs to be normalized or standardized to unify the scale to facilitate model learning), etc.

[0047] The acquisition module in this embodiment implements the first level of data processing and organization, the main control module implements the second level of data processing and cleaning, and the server implements the third level of output processing and cleaning.

[0048] Features that are helpful for fault diagnosis are extracted from the original data through feature extraction strategies. These features will serve as input features of the intelligent fault diagnosis model and provide necessary information for subsequent modeling.

[0049] In some embodiments, mechanical state features, driving state features, process state features, and external factor features are extracted respectively according to a preset feature extraction strategy;

[0050] The extracted multi-source features are weightedly fused through the attention mechanism to dynamically learn the importance of each type of feature for fault prediction.

[0051] Specifically, in this embodiment, the mechanical state features are extracted by analyzing local trend changes in equipment vibration or operation data, and the evolution trend of the equipment state is captured based on the local mean change rate.

[0052] Assume that the device vibration signal is , extract local trend features through the following steps:

[0053] S201. Calculate the local mean: Calculate the local mean of the signal within a sliding window:

[0054]

[0055] Where, Indicates the window width, Indicates location The local mean at .

[0056] S202. Calculate the local mean change rate based on the local mean. The local mean change rate at this time can reflect the state change of the mechanical equipment:

[0057]

[0058] Where, Indicates a time interval.

[0059] The driving state characteristics directly reflect the impact of the driving system on the mechanical equipment. By analyzing the interactive relationship between the driving signal and the equipment response, the driving state can be captured more accurately. This embodiment quantifies the relationship between the driving signal and the equipment response signal based on the mutual information.

[0060] Assume that the driving signal is And the device response signal is , mutual information The calculation formula is as follows:

[0061]

[0062] in, Is the driving signal The entropy of , which represents the uncertainty of the signal; In response to the signal Entropy; Is the driving signal and response signal The joint entropy of .

[0063] By calculation , the impact of the driving signal on the device state can be quantified, thereby extracting the driving features. If the mutual information is large, it indicates that the driving signal has a strong correlation with the device response, which may indicate an abnormal state.

[0064] Process state characteristics are typically determined by factors such as operating conditions and the processing environment. To effectively capture process states, this embodiment constructs a dynamic constraint model to analyze the impact of process parameters on equipment performance and incorporates a machine learning model to identify characteristics under different process states.

[0065] Assume that the process parameters are , which affects the device output , features are extracted by constructing a dynamic constraint model (such as a state-space model).

[0066] The dynamic constraint equation is as follows:

[0067]

[0068] Where, is a model parameter, which represents the impact of process conditions on the equipment.

[0069] Update model parameters using recursive least squares method based on historical data , and calculate process status characteristics in real-time monitoring:

[0070]

[0071] The real-time characteristics of the process status are obtained through changes in model parameters.

[0072] Regarding environmental factor features, this embodiment extracts external factor features based on space-time correlation analysis. By analyzing the temporal changes and spatial distribution of the external environment, the impact of external factors on the device status can be obtained.

[0073] Assume that the external factors are , each factor changes over time, and space-time cross-correlation is used to measure the relationship between external factors and device status:

[0074]

[0075] Where, Represents the cross-correlation coefficient between external factors and device status, and represent the means of external factors and equipment status respectively.

[0076] By analyzing the spatial-temporal correlation within different time windows, the impact characteristics of external factors on equipment status are extracted.

[0077] Based on the features obtained above, an attention network is constructed to learn the importance of each feature vector (specifically reflected by the attention score function). According to the obtained attention weight, all features are weighted summed to obtain the fusion feature.

[0078] Step S103: Monitor the operating status of the steam turbine generator set and diagnose faults based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on the fused multi-source feature data.

[0079] In some embodiments, a data set is constructed based on the fused multi-source feature vectors, and the first network model, the second network model, and the third network model are trained using the data set to obtain the category prediction probability corresponding to each model;

[0080] Define multiple weighting coefficients, perform weighted voting fusion according to the category prediction probability, make the final classification decision based on the weighted probability, and output the fault category and confidence of the steam turbine generator set.

[0081] Specifically, in this embodiment, a data set is constructed based on the fused multi-source feature vectors, and the data set is randomly divided into a training set, a test set, and a validation set. The first network model (i.e., a CNN model), the second network model (i.e., an LSTM model), and the third network model (i.e., XGBoost) are trained separately to obtain the prediction probability corresponding to each model. After obtaining the prediction probability of each model, a weighted sum is performed based on the weighting coefficient, and the final prediction probability is calculated to make the final classification decision (in this embodiment, the category with the highest probability is used as the final prediction result), thereby obtaining the fault category and confidence level of the steam turbine generator set. The specific formula is as follows:

[0082]

[0083] in, 、 、 Represents the weighting coefficient, satisfying the condition + + =1, 、 、 CNN, LSTM and XGoost models for categories The predicted probability of .

[0084] In some embodiments, the method further includes predicting the life of equipment of the steam turbine generator set using a Weibull distribution algorithm, including:

[0085] Collect historical operating data of the equipment and fit the Weibull distribution using the maximum likelihood estimation method to estimate the parameters of the Weibull distribution. Use the estimated Weibull distribution parameters to predict the remaining life of the equipment.

[0086] A trigger threshold is set according to a preset value of the remaining life, and equipment failure monitoring and early warning are performed according to the trigger threshold.

[0087] Specifically, in this embodiment, historical operating data of the equipment, especially the time when the fault occurred, is collected, and the parameters of the Weibull distribution are estimated using the Maximum Likelihood Estimation (MLE). The estimated Weibull distribution parameters are used to predict the remaining life of the equipment, and a trigger threshold is set for the equipment based on the predicted remaining life.

[0088] For example, assuming that the equipment has been running for 200 hours, its remaining life is predicted. For example, it is set that maintenance is required when the remaining life of the equipment is less than 10%.

[0089] Among them, the probability density function of Weibull distribution is as follows:

[0090] ;

[0091] Where, Indicates the life of the equipment; It represents the size parameter, controls the width of the distribution, and determines the scale of the equipment life; Represents shape parameters that control device failure modes. Specifically:

[0092] when When <1, it means that the failure rate of the equipment decreases with time, which is reflected as early failure;

[0093] when =1, indicating that the failure rate of the equipment is constant and conforms to the exponential distribution, which is manifested as random failure;

[0094] when When >1, it means that the failure rate of the equipment increases with time, which is reflected as aging failure.

[0095] In this embodiment, intelligent fault diagnosis and safety assessment of the main components of the steam turbine are important aspects of the smart power plant. The use of the Weibull distribution algorithm to predict equipment reliability is an important means to quantify the prediction results. The prediction results can be used as part of the trigger threshold of the equipment management system, thereby achieving closed-loop control from monitoring to maintenance.

[0096] This embodiment can automatically diagnose the following faults: rotor imbalance, initial imbalance, sudden imbalance, thermal bending imbalance, rotor bending, large radial load, misalignment, friction, local rubbing, full-circumference rubbing, severe friction, looseness, loose mechanical fastening, low base stiffness, fluid excitation instability, turbine water hammer, oil film vortex and oil film oscillation, shaft cracks, and other faults.

[0097] In some embodiments, the multi-channel data acquisition system inspects the sensor status of all input signals. In response to determining that the sensor working status is abnormal, the system no longer collects invalid data and only transmits "sensor status abnormality" information to the system.

[0098] Specifically, in this embodiment, the data authenticity detection function system is implemented to inspect the sensor status of all input signals. When it is determined that the sensor working status is abnormal, the collector no longer collects invalid data, but only transmits the "sensor status abnormality" information to the system to ensure the authenticity of the collected data.

[0099] The intelligent fault diagnosis method for a steam turbine generator set described in this embodiment collects multi-source feature data based on full-state parameter monitoring of the entire process flow of the monitored object, extracts correlation features between different measuring points and different monitoring quantities, and realizes high-precision detection of multi-component and multi-type coupled faults of the unit based on the constructed intelligent fault diagnosis model, ultimately achieving closed-loop control from monitoring to maintenance, and improving equipment reliability and maintenance efficiency.

[0100] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application further provides an intelligent fault diagnosis system for a steam turbine generator set.

[0102] like Figure 2 As shown, the intelligent fault diagnosis system for steam turbine generator sets includes:

[0103] The data acquisition module 11 is configured to acquire the operating status data and external factor data of the steam turbine generator set in real time through a multi-channel data acquisition system; wherein the operating status data includes the mechanical status data, drive status data and process status data of the main engine and auxiliary engines;

[0104] The feature extraction module 12 is configured to extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features;

[0105] The fault diagnosis module 13 is configured to monitor the operating status and diagnose faults of the steam turbine generator set based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on the fused multi-source feature data.

[0106] This embodiment establishes a comprehensive intelligent platform that integrates a real-time online monitoring system for the status of the main unit and related auxiliary equipment of the steam turbine generator set, a real-time intelligent diagnosis system and a management system to realize intelligent operation and maintenance of the main unit and auxiliary equipment. The system adopts a distributed, modular and edge-intelligent hardware platform and a data platform based on industrial big data. At the same time, it takes into account the absolute security of the on-site TSI monitoring system and the highly secure layout of the on-site network system. It can use the big data platform to realize intelligent prediction of unit operation data and faults, intelligent alarm and other functions; at the same time, it can provide comprehensive routine monitoring and analysis tools for use by dedicated technical personnel.

[0107] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0108] The system of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0109] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method described in any of the above embodiments is implemented.

[0110] Figure 3 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0111] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0112] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0113] The input / output interface 1030 is used to connect to an input / output module to enable information input and output. The input / output module can be configured as a component within the device (not shown) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc. Output devices may include a display, speaker, vibrator, indicator light, etc.

[0114] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.).

[0115] The bus 1050 comprises a pathway for transmitting information between various components of the device, such as the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 .

[0116] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0117] The electronic device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0118] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in any of the above embodiments.

[0119] The computer-readable media of this embodiment includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0120] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0121] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0122] In addition, to simplify the description and discussion, and to avoid obscuring the understanding of the embodiments of the present application, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. Furthermore, devices may be shown in block diagram form to avoid obscuring the understanding of the embodiments of the present application, and this also takes into account the fact that the implementation details of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (i.e., these details should be fully understood by those skilled in the art). Where specific details (e.g., circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations therefrom. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0123] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the discussed embodiments.

[0124] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A method for intelligent fault diagnosis of a steam turbine generator set, characterized in that: include: Acquire the operating status data and external factor data of the steam turbine generator set in real time through a multi-channel data acquisition system; wherein the operating status data includes the mechanical status data, drive status data and process status data of the main engine and auxiliary engine; Extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features; The operating status of the steam turbine generator set is monitored and fault diagnosis is performed based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on fused multi-source feature data.

2. The method according to claim 1, wherein: The mechanical state data includes at least mechanical vibration, noise, axial displacement, bearing temperature, eccentricity and thermal expansion; The process status data at least includes flow rate, pressure and valve position; The driving state data at least includes a driving medium state and a driving output state; The external factor data at least includes pipeline network impact, lubrication status and environmental factors.

3. The method according to claim 1, wherein: The multi-channel data acquisition system inspects the sensor status of all input signals. In response to determining that the sensor working status is abnormal, the system no longer collects invalid data and only transmits the "sensor status abnormality" information to the system.

4. The method according to claim 1, wherein: According to the preset feature extraction strategy, mechanical state features, driving state features, process state features and external factor features are extracted respectively; The extracted multi-source features are weightedly fused through the attention mechanism to dynamically learn the importance of each type of feature for fault prediction.

5. The method according to claim 4, characterized in that: Constructing a data set based on the fused multi-source feature vectors, and respectively training the first network model, the second network model, and the third network model using the data set to obtain the category prediction probability corresponding to each model; A plurality of weighting coefficients are defined, and weighted voting fusion is performed according to the category prediction probability. A final classification decision is made according to the weighted probability, and the fault category and confidence level of the steam turbine generator set are output.

6. The method according to claim 1, characterized in that ； It also includes the life prediction of steam turbine generator equipment using the Weibull distribution algorithm, including: Collect historical operating data of the equipment and fit the Weibull distribution using the maximum likelihood estimation method to estimate the parameters of the Weibull distribution. Use the estimated Weibull distribution parameters to predict the remaining life of the equipment. A trigger threshold is set according to a preset value of the remaining life, and equipment failure monitoring and early warning are performed according to the trigger threshold.

7. The method according to claim 6, characterized in that The probability density function of the Weibull distribution is as follows: ； Where, Indicates the life of the equipment. Represents the size parameter, Represents the shape parameter.

8. An intelligent fault diagnosis system for a steam turbine generator set, characterized in that: include: a data acquisition module configured to acquire, in real time, operating status data and external factor data of the steam turbine generator set through a multi-channel data acquisition system; wherein the operating status data includes mechanical status data, drive status data, and process status data of the main engine and auxiliary engines; The feature extraction module is configured to extract multi-source features from the original data obtained after data preprocessing, and perform feature fusion on the extracted multi-source features; The fault diagnosis module is configured to monitor the operating status and diagnose faults of the steam turbine generator set based on a pre-built intelligent fault diagnosis model, wherein the intelligent fault diagnosis model is a deep learning neural network model trained based on the fused multi-source feature data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A non-transitory computer-readable storage medium, characterized in that in, The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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