Generator set fault diagnosis method and system based on CAE simulation

Abstract

The invention discloses a generator set fault diagnosis method and system based on CAE simulation, and relates to the technical field of fault diagnosis, and the method comprises the steps: carrying out the characteristic analysis and three-dimensional modeling of the structural design information and operation environment information of a water-turbine generator set, constructing a water-turbine generator set model as a basic unit simulation parameter, and carrying out the simulation of a water-turbine generator set; the collected and obtained standard generator set operation data flow serves as momentum unit simulation parameters, simulation operation simulation is conducted on the basic unit simulation parameters and the momentum unit simulation parameters through the CAE simulation technology, and a unit component simulation parameter set is obtained for cloud picture visual identification display and fault analysis and diagnosis. And obtaining a generator set fault diagnosis result to perform operation and maintenance simulation management and control on the water intake turbine generator set. The technical effects that the running state of the unit is simulated through the CAE simulation technology, efficient and accurate diagnosis of unit faults is achieved, the unit diagnosis application range is widened, and diagnosis convenience is improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a method and system for diagnosing faults of a generator set based on CAE simulation. Background Art

[0002] As an important equipment for hydropower generation, the stability and safety of the operating state of the hydropower generator set is directly related to the reliability and economy of the power supply. Due to the complex structure of the generator set and the harsh operating environment, various faults are inevitable during the operation of the unit. However, the existing fault diagnosis methods rely on manual inspection and instrument detection, which have the problems of long diagnosis cycle and low diagnosis accuracy. Summary of the invention

[0003] The present application solves the technical problems of long fault diagnosis cycle and low diagnostic accuracy in the prior art by providing a generator set fault diagnosis method and system based on CAE simulation, and achieves the technical effect of simulating the unit operating status by using CAE simulation technology, realizing efficient and accurate diagnosis of unit faults, and improving the scope of application and convenience of unit diagnosis.

[0004] In view of the above problems, the present invention provides a generator set fault diagnosis method and system based on CAE simulation.

[0005] In a first aspect, the present application provides a generator set fault diagnosis method based on CAE simulation, the method comprising: S1: obtaining structural design information and operating environment information of a hydro-turbine generator set, performing characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and constructing a hydro-turbine generator set model; S2: acquiring the generator set operating data stream through the unit monitoring sensor, performing standardized preprocessing on the generator set operating data stream, obtaining the standard generator set operating data stream, using the hydro-turbine generator set model as the basic unit simulation parameters, and using the standard generator set operating data stream as the momentum unit simulation parameters; S3: using CAE simulation technology to perform simulation operation simulation on the basic unit simulation parameters and the momentum unit simulation parameters, and obtaining a set of unit component simulation parameters for cloud map visualization and identification display; S4: performing fault analysis and diagnosis on the set of unit component simulation parameters through the generator set safety threshold, obtaining the generator set fault diagnosis result, and performing operation and maintenance simulation management and control on the hydro-turbine generator set according to the generator set fault diagnosis result.

[0006] On the other hand, the present application also provides a generator set fault diagnosis system based on CAE simulation, the system comprising: a unit model construction module, used to obtain structural design information and operating environment information of the hydro-turbine generator set, perform characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and construct a hydro-turbine generator set model; a simulation parameter acquisition module, used to acquire the generator set operation data stream through the unit monitoring sensor, perform standardized preprocessing on the generator set operation data stream, obtain the standard generator set operation data stream, use the hydro-turbine generator set model as the basic unit simulation parameters, and use the standard generator set operation data stream as the momentum unit simulation parameters; a simulation operation simulation module, used to use CAE simulation technology to perform simulation operation simulation on the basic unit simulation parameters and the momentum unit simulation parameters, obtain the unit component simulation parameter set for cloud map visualization and identification display; an operation and maintenance simulation control module, used to perform fault analysis and diagnosis on the unit component simulation parameter set through the generator set safety threshold, obtain the generator set fault diagnosis result, and perform operation and maintenance simulation control on the hydro-turbine generator set according to the generator set fault diagnosis result.

[0007] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0008] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The invention adopts the method of constructing a hydro-generator set model by analyzing the structural design information and operating environment information of the hydro-generator set and performing standard preprocessing on the collected operating data stream of the generator set, obtaining the standard operating data stream of the generator set, using the hydro-generator set model as the basic unit simulation parameters, using the standard operating data stream of the generator set as the momentum unit simulation parameters, using CAE simulation technology to simulate the basic unit simulation parameters and the momentum unit simulation parameters, obtaining the set of unit component simulation parameters for cloud map visualization and identification display, and then performing fault analysis and diagnosis on the set of unit component simulation parameters through the safety threshold of the generator set, obtaining the generator set fault diagnosis results, and performing operation and maintenance simulation control on the hydro-generator set. The technical effect of simulating the unit operating state by using CAE simulation technology, realizing efficient and accurate diagnosis of unit faults, and improving the scope of application and convenience of unit diagnosis is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of a flow chart of a generator set fault diagnosis method based on CAE simulation in this application;

[0013] Figure 2 This is a schematic diagram of the structure of the generator set fault diagnosis system based on CAE simulation in this application;

[0014] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0015] Explanation of the reference numerals: unit model building module 11, simulation parameter acquisition module 12, simulation operation simulation module 13, operation and maintenance simulation control module 14, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application 1152 and user interface 1160. DETAILED DESCRIPTION

[0016] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0017] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0018] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0019] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0020] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0021] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0022] The present application is described below in conjunction with the drawings in the present application.

[0023] Embodiment 1

[0024] like Figure 1 As shown, the present application provides a generator set fault diagnosis method based on CAE simulation, the method comprising:

[0025] Step S1: acquiring structural design information and operating environment information of a hydro-generator set, performing characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and constructing a hydro-generator set model;

[0026] Furthermore, the step of constructing the hydro-generator set model in S1 also includes:

[0027] S11: performing functional structure disassembly and attribute marking on the structural design information to obtain a set of unit component attribute characteristic data, and performing component connection function analysis on the set of unit component attribute characteristic data to obtain unit component connection characteristic data information;

[0028] S12: using three-dimensional modeling technology to perform three-dimensional modeling on the unit component attribute characteristic data set and the unit component connection characteristic data information to generate a unit component connection model;

[0029] S13: performing multi-dimensional characteristic analysis on the operating environment information, obtaining multi-dimensional characteristic parameter information of the operating environment, and constructing an operating environment characteristic model according to the multi-dimensional characteristic parameter information of the operating environment;

[0030] S14: Obtain target modeling requirements, and based on the target modeling requirements, fuse and derive the unit component connection model and the operating environment characteristic model to construct the hydro-generator unit model.

[0031] Furthermore, the unit component attribute characteristic data set is obtained in S11, and the steps of the present application also include:

[0032] Performing functional structural disassembly on the structural design information to obtain a set of unit structural components;

[0033] Acquiring unit component attribute element information, wherein the unit component attribute element information includes structural dimensions, material attributes, and mechanical characteristics;

[0034] Performing content extraction and deepening on the unit component attribute element information to obtain a component attribute content classification node set, and constructing a structural attribute classifier based on the component attribute content classification node set;

[0035] The structural attribute classifier is used to classify and mark the attributes of each component in the set of unit structural components to obtain the set of unit component attribute characteristic data.

[0036] Specifically, in order to achieve efficient fault diagnosis of a hydro-turbine generator set, firstly, the structural design information of the hydro-turbine generator set is obtained through the hydro-turbine design drawings and technical documents, and at the same time, the actual operating environment information of the hydro-turbine generator set is obtained through monitoring, and then the structural design information and operating environment information are analyzed and three-dimensionally modeled. Specifically, the functional structure of the structural design information is disassembled and the attribute is marked. First, the structure of the unit is disassembled according to the application function of each component of the unit of the structural design information, and the corresponding set of unit structural components is obtained. The set of unit structural components includes rotors, stators, frames, brakes, thrust bearings, etc. Then, the unit component attribute element information is determined, and the unit component attribute element information is the component attribute classification index information, including structural dimensions, material attributes, and mechanical characteristics.

[0037] The unit component attribute element information is extracted and deepened, that is, the specific classification content information of each unit component attribute element is extracted through the unit component attribute database. For example, for mechanical properties, the specific classification content includes tensile strength, impact strength, etc. Each specific classification content is used as a content classification node to obtain a component attribute content classification node set. A structural attribute classifier is constructed based on the component attribute content classification node set, and the structural attribute classifier is used to mark the unit component attributes with specific parameters in turn according to the content classification nodes. The structural attribute classifier is used to classify and mark the attributes of each component in the unit structure component set to obtain the corresponding unit component attribute characteristic data set. The unit component attribute characteristic data set is analyzed for component connection mode and interaction through the structural design information, such as shaft connection, bearing support, etc., to obtain the corresponding unit component connection characteristic data information.

[0038] The three-dimensional modeling technology is used to perform three-dimensional modeling on the set component attribute characteristic data set and the set component connection characteristic data information to generate a set component connection model, which is used to display the structural component attributes and connection relationships of the hydro-generator set. Then, the operating environment information is subjected to multi-dimensional characteristic analysis to obtain the operating environment multi-dimensional characteristic parameter information, which is the associated environmental parameters affecting the operation of the set, including water flow velocity, direction, temperature, humidity, etc. An operating environment characteristic model is constructed according to the operating environment multi-dimensional characteristic parameter information, which is used to display the operating environment impact information of the hydro-generator set. Then, the target modeling requirements are obtained, and the target modeling requirements include model accuracy and simulation function requirements. Based on the target modeling requirements, the set component connection model and the operating environment characteristic model are merged and exported to obtain a three-dimensional modeled hydro-generator set model, which is used as the basis for the subsequent operation simulation model of the hydro-generator set. The modeling accuracy and comprehensiveness of the hydro-generator set model are improved, thereby improving the simulation accuracy of the subsequent model.

[0039] Step S2: acquiring the generator set operation data stream through the unit monitoring sensor, performing standardization preprocessing on the generator set operation data stream, acquiring the standard generator set operation data stream, using the hydro-generator set model as the basic unit simulation parameter, and using the standard generator set operation data stream as the momentum unit simulation parameter;

[0040] Specifically, the generator set operation data stream of the hydro-turbine generator set in actual operation is collected by the unit monitoring sensor, wherein the unit monitoring sensor is a multi-type sensor group installed on the hydro-turbine generator set, including flow, temperature, stress, vibration sensors, etc. In order to ensure the quality of data application, the generator set operation data stream is subjected to standardized preprocessing, including filtering or correcting abnormal values, data normalization processing, etc., to obtain the preprocessed standard generator set operation data stream to ensure data accuracy and consistency. The hydro-turbine generator set model is used as the basic unit simulation parameter, and the standard generator set operation data stream is used as the momentum unit simulation parameter for the unit simulation parameter configuration to ensure that the model simulation parameters are consistent with the actual operation of the unit.

[0041] Step S3: using CAE simulation technology to simulate the basic unit simulation parameters and the momentum unit simulation parameters, and obtaining the unit component simulation parameter set for cloud map visualization and identification display;

[0042] Furthermore, the simulation parameter set of the unit components is obtained in S3 for cloud map visualization and identification display, and the steps of this application also include:

[0043] S31: Perform simulation modeling and finite element meshing on the basic unit simulation parameters and the momentum unit simulation parameters using CAE simulation technology to generate a generator unit simulation model;

[0044] S32: performing operation simulation calculation based on the generator set simulation model to obtain the generator set operation simulation result, and extracting key parameters of the generator set operation simulation result to obtain the set of simulation parameters of the generator set components;

[0045] S33: acquiring a key parameter hierarchical division rule according to the generator set application standard, performing parameter hierarchical division on the acquired generator set component simulation parameter set based on the key parameter hierarchical division rule, and determining hierarchical distribution information of the generator set component simulation parameter;

[0046] S34: Drawing and displaying the simulation model of the generator set by cloud mapping based on the hierarchical distribution information of the simulation parameters of the generator set components.

[0047] Specifically, the CAE simulation technology is used to simulate and model the simulation parameters of the basic unit and the simulation parameters of the momentum unit, and then the modeling simulation model is meshed by finite elements. The density and quality of the mesh directly affect the accuracy of the simulation results, and the meshed generator set simulation model is generated. According to the simulation function requirements, such as unit operation failures, operation simulation calculations are performed based on the generator set simulation model to obtain the generator set operation simulation results. The generator set operation simulation results are the operation simulation failure prediction conditions of the hydro-generator set, including operation simulation data streams such as operation stress, temperature, and vibration. And the key parameters of the generator set operation simulation results are extracted to obtain the unit component simulation parameter set. The unit component simulation parameter set is the operation parameter information that is highly correlated with the generator set operation status, such as operation stress, temperature, vibration and other parameters.

[0048] According to the application standard of the generator set, the key parameter level division rule is obtained. The key parameter level division rule is the parameter degree level divided according to the operating data standard of the hydro-turbine generator set. For example, according to the operating temperature distribution range of the hydro-turbine generator set, different operating temperature levels are obtained by empirical division. Based on the key parameter level division rule, the level of each simulation parameter in the obtained unit component simulation parameter set is divided, and the unit component simulation parameter level distribution information is determined. The unit component simulation parameter level distribution information is the simulation parameter operation level distribution information of each component in the generator set. Then, the generator set simulation model is displayed by cloud mapping through the unit component simulation parameter level distribution information. For example, the cloud map color configuration is performed according to the parameter level, and the unit simulation results are visually displayed to improve the accuracy of the simulation results and facilitate fault analysis and diagnosis.

[0049] Step S4: performing fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generator set to obtain the fault diagnosis result of the generator set, and performing operation and maintenance simulation management and control on the water turbine generator set according to the fault diagnosis result of the generator set.

[0050] Furthermore, in the step S4, the generator set fault diagnosis result is obtained, and the steps of the present application further include:

[0051] S41: Acquire unit fault diagnosis factors, wherein the unit fault diagnosis factors include fault mode, fault level and generation cause, and classify and identify the generator unit operation fault data set according to the unit fault diagnosis factors to obtain the unit operation fault factor data set;

[0052] S42: using a deep neural network to train and average the data set of unit operation fault factors to build a unit operation fault diagnosis model;

[0053] S43: performing fault analysis and diagnosis on the set of unit component simulation parameters based on the unit operation fault diagnosis model to obtain the generator set fault diagnosis result, wherein the generator set fault diagnosis result includes a fault diagnosis mode, a fault diagnosis level and a fault generation cause.

[0054] Furthermore, the operation and maintenance simulation control of the water turbine generator set is performed according to the fault diagnosis result of the generator set, and the steps of the present application also include:

[0055] Acquire the generator set operation and maintenance control strategy, perform similarity matching based on the generator set fault diagnosis result and the generator set operation and maintenance control strategy, and determine the generator set fault operation and maintenance strategy;

[0056] According to the unit fault operation and maintenance strategy, construct the unit operation and maintenance parameter control space;

[0057] Parameter optimization analysis is performed within the unit operation and maintenance parameter control space to obtain unit operation and maintenance optimization control parameters, and operation and maintenance optimization management and control of the hydro-generator unit is performed based on the unit operation and maintenance optimization control parameters.

[0058] Furthermore, the application steps also include:

[0059] Performing operation and maintenance iterative simulation on the operation and maintenance optimization control parameters of the generator set through the generator set simulation model to obtain the operation and maintenance feedback effect of the generator set;

[0060] If the operation and maintenance feedback effect of the generator set does not reach the preset operation and maintenance effect, obtain the optimization parameter variation direction, and set the parameter variation rule according to the optimization parameter variation direction;

[0061] Based on the parameter variation rules, the unit operation and maintenance optimization control parameters are mutated and fine-tuned for optimization.

[0062] Specifically, the fault analysis and diagnosis of the set of simulation parameters of the unit components is performed through the safety threshold of the generator set, wherein the safety threshold of the generator set is the safe operating parameter range of the hydro-turbine generator set. First, the unit fault diagnosis factor is obtained, and the unit fault diagnosis factor is a fault diagnosis evaluation index, including a fault mode, a fault level, and a generation cause. The historical fault data is collected through big data to obtain the corresponding generator set operation fault data set, and the generator set operation fault data set is classified and identified according to the unit fault diagnosis factor. The data set is classified and integrated according to the type of unit fault diagnosis factor to obtain a unit operation fault factor data set, and the unit operation fault factor data set includes a fault mode factor data set, a fault level factor data set, and a generation cause factor data set.

[0063] The fault analysis training of the unit operation fault factor data set is performed by using a deep neural network until convergence, and the corresponding fault mode analysis model, fault level analysis model and generation cause analysis model are obtained. Then, the fault mode analysis model, fault level analysis model and generation cause analysis model are weighted and fused equally to construct a unit operation fault diagnosis model, which is used to perform comprehensive fault analysis diagnosis according to the unit operation parameters. Based on the unit operation fault diagnosis model, the unit component simulation parameter set is subjected to fault analysis and diagnosis, and the generator set fault diagnosis result is output, which is the operation fault analysis of the hydro-generator unit components, including the fault diagnosis mode, fault diagnosis level and fault generation cause. Efficient and accurate diagnosis of unit faults is achieved, and the scope of application and convenience of unit diagnosis are improved.

[0064] According to the fault diagnosis result of the generator set, the operation and maintenance simulation control of the hydro-generator set is performed. First, the operation and maintenance control strategy of the generator set is obtained through the historical operation and maintenance experience of the unit. The operation and maintenance control strategy of the generator set is a set of fault solution strategies for the hydro-generator set, including improving structural design, optimizing material selection, adjusting working conditions, adjusting operating parameters, etc. Based on the similarity matching between the fault diagnosis result of the generator set and the operation and maintenance control strategy of the generator set, the strategy scheme with the highest similarity to the fault diagnosis result of the generator set in the operation and maintenance control strategy of the generator set can be calculated and screened by a similarity algorithm, and determined as the unit fault operation and maintenance strategy. Further, according to the unit fault operation and maintenance strategy, the historical specific operation and maintenance data of the same operation and maintenance strategy is searched to construct the unit operation and maintenance parameter control space. The unit operation and maintenance parameter control space is the optimization range of specific operation and maintenance control parameters, including historical unit fault operation and maintenance control parameters and corresponding operation and maintenance effect data. Parameter optimization analysis is performed in the unit operation and maintenance parameter control space, and the fault operation and maintenance control parameters with the best operation and maintenance effect are obtained by comparison as the unit operation and maintenance optimization control parameters. And based on the unit operation and maintenance optimization control parameters, the operation and maintenance of the water turbine generator set is optimized and controlled to achieve efficient and accurate fault operation and maintenance analysis, thereby improving the unit operation and maintenance effect and efficiency.

[0065] In order to ensure the application effect of the operation and maintenance control parameters, the operation and maintenance optimization control parameters of the unit are subjected to operation and maintenance iteration simulation through the generator set simulation model, and the optimization scheme is applied to the simulation model for analysis to obtain the operation and maintenance feedback effect of the generator set predicted by simulation. If the operation and maintenance feedback effect of the generator set does not reach the preset operation and maintenance effect, it indicates that the operation and maintenance optimization control parameters of the unit need to be optimized. By comparing and analyzing the operation and maintenance feedback effect of the generator set, the performance of the unit to be optimized that does not reach the preset operation and maintenance effect is determined, and then the variation direction of the optimization parameter is obtained through the performance of the unit to be optimized. The variation direction of the optimization parameter is the type of associated operation and maintenance control parameter related to the performance of the unit to be optimized in the unit operation and maintenance optimization control parameters. And according to the variation direction of the optimization parameter, the parameter variation rule is set, and the parameter variation rule is the basis for adjusting the variation of the parameter value of the associated operation and maintenance control parameter type, for example, the parameter value is fine-tuned and varied according to the normal distribution. Based on the parameter variation rule, the unit operation and maintenance optimization control parameters are variably fine-tuned and optimized, and multiple optimized unit operation and maintenance optimization control parameters are obtained, and then the operation and maintenance effect of multiple unit operation and maintenance optimization control parameters is optimized. Improve the analysis accuracy and practical applicability of unit operation and maintenance control parameters, thereby ensuring the operation and maintenance effect of the generator set.

[0066] In summary, the generator set fault diagnosis method based on CAE simulation provided by this application has the following technical effects:

[0067] The invention adopts the method of constructing a hydro-generator set model by analyzing the structural design information and operating environment information of the hydro-generator set and performing standard preprocessing on the collected operating data stream of the generator set, obtaining the standard operating data stream of the generator set, using the hydro-generator set model as the basic unit simulation parameters, using the standard operating data stream of the generator set as the momentum unit simulation parameters, using CAE simulation technology to simulate the basic unit simulation parameters and the momentum unit simulation parameters, obtaining the set of unit component simulation parameters for cloud map visualization and identification display, and then performing fault analysis and diagnosis on the set of unit component simulation parameters through the safety threshold of the generator set, obtaining the generator set fault diagnosis results, and performing operation and maintenance simulation control on the hydro-generator set. The technical effect of simulating the unit operating state by using CAE simulation technology, realizing efficient and accurate diagnosis of unit faults, and improving the scope of application and convenience of unit diagnosis is achieved.

[0068] Embodiment 2

[0069] Based on the same inventive concept as the generator set fault diagnosis method based on CAE simulation in the aforementioned embodiment, the present invention also provides a generator set fault diagnosis system based on CAE simulation, such as Figure 2 As shown, the system comprises:

[0070] The unit model building module 11 is used to obtain the structural design information and operating environment information of the hydro-generator unit, perform characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and build a hydro-generator unit model;

[0071] The simulation parameter acquisition module 12 is used to acquire the generator set operation data stream through the unit monitoring sensor, perform standardization preprocessing on the generator set operation data stream, acquire the standard generator set operation data stream, use the hydro-generator set model as the basic unit simulation parameter, and use the standard generator set operation data stream as the momentum unit simulation parameter;

[0072] A simulation operation module 13 is used to perform simulation operation on the basic unit simulation parameters and the momentum unit simulation parameters using CAE simulation technology, and obtain a set of unit component simulation parameters for cloud map visualization and identification display;

[0073] The operation and maintenance simulation control module 14 is used to perform fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generator set, obtain the fault diagnosis result of the generator set, and perform operation and maintenance simulation control on the water turbine generator set according to the fault diagnosis result of the generator set.

[0074] Furthermore, the unit model building module 11 is also used for:

[0075] Performing functional structure disassembly and attribute marking on the structural design information to obtain a set of unit component attribute characteristic data, and performing component connection function analysis on the set of unit component attribute characteristic data to obtain unit component connection characteristic data information;

[0076] Using three-dimensional modeling technology to perform three-dimensional modeling on the unit component attribute characteristic data set and the unit component connection characteristic data information to generate a unit component connection model;

[0077] Performing multi-dimensional characteristic analysis on the operating environment information to obtain multi-dimensional characteristic parameter information of the operating environment, and constructing an operating environment characteristic model according to the multi-dimensional characteristic parameter information of the operating environment;

[0078] Obtain target modeling requirements, and based on the target modeling requirements, fuse and derive the unit component connection model and the operating environment characteristic model to construct the hydro-generator unit model.

[0079] Furthermore, the unit model building module 11 is also used for:

[0080] Performing functional structural disassembly on the structural design information to obtain a set of unit structural components;

[0081] Acquiring unit component attribute element information, wherein the unit component attribute element information includes structural dimensions, material attributes, and mechanical characteristics;

[0082] Performing content extraction and deepening on the unit component attribute element information to obtain a component attribute content classification node set, and constructing a structural attribute classifier based on the component attribute content classification node set;

[0083] The structural attribute classifier is used to classify and mark the attributes of each component in the set of unit structural components to obtain the set of unit component attribute characteristic data.

[0084] Furthermore, the simulation operation module 13 is also used for:

[0085] Using CAE simulation technology to perform simulation modeling and finite element meshing on the basic unit simulation parameters and the momentum unit simulation parameters to generate a generator unit simulation model;

[0086] Performing operation simulation calculation based on the generator set simulation model to obtain the generator set operation simulation result, and extracting key parameters of the generator set operation simulation result to obtain the set of simulation parameters of the generator set components;

[0087] Acquire a key parameter hierarchical division rule according to the generator set application standard, perform parameter hierarchical division on the acquired generator set component simulation parameter set based on the key parameter hierarchical division rule, and determine the hierarchical distribution information of the generator set component simulation parameter;

[0088] The generator set simulation model is displayed by drawing a cloud map based on the hierarchical distribution information of the simulation parameters of the unit components.

[0089] Furthermore, the operation and maintenance simulation control module 14 is also used for:

[0090] Acquire unit fault diagnosis factors, wherein the unit fault diagnosis factors include fault mode, fault level and generation cause, and classify and identify the generator unit operation fault data set according to the unit fault diagnosis factors to obtain the unit operation fault factor data set;

[0091] Using a deep neural network to train and average the data set of the unit operation fault factors to build a unit operation fault diagnosis model;

[0092] Based on the unit operation fault diagnosis model, fault analysis and diagnosis are performed on the unit component simulation parameter set to obtain the generator set fault diagnosis result, which includes a fault diagnosis mode, a fault diagnosis level and a fault generation cause.

[0093] Furthermore, the operation and maintenance simulation control module 14 is also used for:

[0094] Acquire the generator set operation and maintenance control strategy, perform similarity matching based on the generator set fault diagnosis result and the generator set operation and maintenance control strategy, and determine the generator set fault operation and maintenance strategy;

[0095] According to the unit fault operation and maintenance strategy, construct the unit operation and maintenance parameter control space;

[0096] Parameter optimization analysis is performed within the unit operation and maintenance parameter control space to obtain unit operation and maintenance optimization control parameters, and operation and maintenance optimization management and control of the hydro-generator unit is performed based on the unit operation and maintenance optimization control parameters.

[0097] Furthermore, the operation and maintenance simulation control module 14 is also used for:

[0098] Performing operation and maintenance iterative simulation on the operation and maintenance optimization control parameters of the generator set through the generator set simulation model to obtain the operation and maintenance feedback effect of the generator set;

[0099] If the operation and maintenance feedback effect of the generator set does not reach the preset operation and maintenance effect, obtain the optimization parameter variation direction, and set the parameter variation rule according to the optimization parameter variation direction;

[0100] Based on the parameter variation rules, the unit operation and maintenance optimization control parameters are mutated and fine-tuned for optimization.

[0101] The foregoing Figure 1 The various variations and specific examples of the generator set fault diagnosis method based on CAE simulation in Example 1 are also applicable to the generator set fault diagnosis system based on CAE simulation in this embodiment. Through the above detailed description of the generator set fault diagnosis method based on CAE simulation, those skilled in the art can clearly know the implementation method of the generator set fault diagnosis system based on CAE simulation in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0102] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0103] Exemplary Electronic Devices

[0104] For details, see Figure 3 As shown, the present application also provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0105] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned method embodiment for controlling output data is implemented.

[0106] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0107] In the present application, a bus architecture (represented by bus 1110 ) may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150 .

[0108] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Such architectures include, by way of example and not limitation, an Industry Standard Architecture bus, a Micro Channel Architecture bus, an expansion bus, a Video Electronics Standards Association, a Peripheral Component Interconnect bus.

[0109] The processor 1120 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processors include: a general-purpose processor, a central processing unit, a network processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, a complex programmable logic device, a programmable logic array, a microcontroller unit or other programmable logic device, a discrete gate, a transistor logic device, and a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in this application may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0110] Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in the present application may be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium known in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, a register, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0111] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, this application will not further describe them.

[0112] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, or a stylus.

[0113] It should be understood that in the present application, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above network may be a self-organizing network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless network, a wireless fidelity network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network may be a global mobile communication device, a code division multiple access device, a global microwave interconnection access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, a LTE frequency division duplex device, a LTE time division duplex device, an advanced long term evolution device, a universal mobile communication device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, and the like.

[0114] It should be understood that the memory 1150 in the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory.

[0115] Volatile memory includes random access memory, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronously linked dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the present application includes, but is not limited to, the above and any other suitable types of memory.

[0116] In the present application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0117] Specifically, the operating system 1151 includes various device programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program implementing the method of the present application may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer device executable instructions that perform specific tasks or implement specific abstract data types.

[0118] In addition, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned method embodiment for controlling output data are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0119] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A generator set fault diagnosis method based on CAE simulation, characterized in that: The method comprises: S1: Acquire structural design information and operating environment information of the hydro-generator set, perform characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and construct a hydro-generator set model; S2: acquiring the generator set operation data stream through the unit monitoring sensor, performing standardization preprocessing on the generator set operation data stream, acquiring the standard generator set operation data stream, using the hydro-turbine generator set model as the basic unit simulation parameter, and using the standard generator set operation data stream as the momentum unit simulation parameter; S3: Using CAE simulation technology to simulate the basic unit simulation parameters and the momentum unit simulation parameters, and obtain the unit component simulation parameter set for cloud map visualization and identification display; S4: Performing fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generator set to obtain a fault diagnosis result of the generator set, and performing operation and maintenance simulation management and control on the water turbine generator set according to the fault diagnosis result of the generator set.

2. The method according to claim 1, characterized in that The hydro-generator set model is constructed in S1, including: S11: performing functional structure disassembly and attribute marking on the structural design information to obtain a set of unit component attribute characteristic data, and performing component connection function analysis on the set of unit component attribute characteristic data to obtain unit component connection characteristic data information; S12: using three-dimensional modeling technology to perform three-dimensional modeling on the unit component attribute characteristic data set and the unit component connection characteristic data information to generate a unit component connection model; S13: performing multi-dimensional characteristic analysis on the operating environment information, obtaining multi-dimensional characteristic parameter information of the operating environment, and constructing an operating environment characteristic model according to the multi-dimensional characteristic parameter information of the operating environment; S14: Obtain target modeling requirements, and based on the target modeling requirements, fuse and derive the unit component connection model and the operating environment characteristic model to construct the hydro-generator unit model.

3. The method according to claim 2, characterized in that The unit component attribute characteristic data set obtained in S11 includes: Performing functional structural disassembly on the structural design information to obtain a set of unit structural components; Acquiring unit component attribute element information, wherein the unit component attribute element information includes structural dimensions, material attributes, and mechanical characteristics; Performing content extraction and deepening on the unit component attribute element information to obtain a component attribute content classification node set, and constructing a structural attribute classifier based on the component attribute content classification node set; The structural attribute classifier is used to classify and mark the attributes of each component in the set of unit structural components to obtain the set of unit component attribute characteristic data.

4. The method according to claim 1, characterized in that The step S3 obtains the set of simulation parameters of the unit components for cloud chart visualization and identification display, including: S31: Perform simulation modeling and finite element meshing on the basic unit simulation parameters and the momentum unit simulation parameters using CAE simulation technology to generate a generator unit simulation model; S32: performing operation simulation calculation based on the generator set simulation model to obtain the generator set operation simulation result, and extracting key parameters of the generator set operation simulation result to obtain the set of simulation parameters of the generator set components; S33: acquiring a key parameter hierarchical division rule according to the generator set application standard, performing parameter hierarchical division on the acquired generator set component simulation parameter set based on the key parameter hierarchical division rule, and determining hierarchical distribution information of the generator set component simulation parameter; S34: Drawing and displaying the simulation model of the generator set by cloud mapping based on the hierarchical distribution information of the simulation parameters of the generator set components.

5. The method according to claim 1, characterized in that The generator set fault diagnosis result is obtained in S4, including: S41: Acquire unit fault diagnosis factors, wherein the unit fault diagnosis factors include fault mode, fault level and generation cause, and classify and identify the generator unit operation fault data set according to the unit fault diagnosis factors to obtain the unit operation fault factor data set; S42: using a deep neural network to train and average the data set of unit operation fault factors to build a unit operation fault diagnosis model; S43: performing fault analysis and diagnosis on the set of unit component simulation parameters based on the unit operation fault diagnosis model to obtain the generator set fault diagnosis result, wherein the generator set fault diagnosis result includes a fault diagnosis mode, a fault diagnosis level and a fault generation cause.

6. The method according to claim 4, characterized in that The operation and maintenance simulation control of the water turbine generator set according to the fault diagnosis result of the generator set includes: Acquire the generator set operation and maintenance control strategy, perform similarity matching based on the generator set fault diagnosis result and the generator set operation and maintenance control strategy, and determine the generator set fault operation and maintenance strategy; According to the unit fault operation and maintenance strategy, construct the unit operation and maintenance parameter control space; Parameter optimization analysis is performed within the unit operation and maintenance parameter control space to obtain unit operation and maintenance optimization control parameters, and operation and maintenance optimization management and control of the hydro-generator unit is performed based on the unit operation and maintenance optimization control parameters.

7. The method according to claim 6, characterized in that The method comprises: Performing operation and maintenance iterative simulation on the operation and maintenance optimization control parameters of the generator set through the generator set simulation model to obtain the operation and maintenance feedback effect of the generator set; If the operation and maintenance feedback effect of the generator set does not reach the preset operation and maintenance effect, obtain the optimization parameter variation direction, and set the parameter variation rule according to the optimization parameter variation direction; Based on the parameter variation rules, the unit operation and maintenance optimization control parameters are mutated and fine-tuned for optimization.

8. The generator set fault diagnosis system based on CAE simulation is characterized by: A system for implementing the generator set fault diagnosis method based on CAE simulation according to any one of claims 1 to 7, the system comprising: A unit model building module is used to obtain structural design information and operating environment information of the hydro-generator unit, perform characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and build a hydro-generator unit model; A simulation parameter acquisition module is used to acquire the generator set operation data stream through the unit monitoring sensor, perform standardization preprocessing on the generator set operation data stream, acquire the standard generator set operation data stream, use the hydro-generator set model as the basic unit simulation parameter, and use the standard generator set operation data stream as the momentum unit simulation parameter; A simulation operation module is used to use CAE simulation technology to perform simulation operation on the basic unit simulation parameters and the momentum unit simulation parameters, and obtain the unit component simulation parameter set for cloud map visualization and identification display; The operation and maintenance simulation control module is used to perform fault analysis and diagnosis on the simulation parameter set of the unit components through the safety threshold of the generator set, obtain the fault diagnosis result of the generator set, and perform operation and maintenance simulation control on the water turbine generator set according to the fault diagnosis result of the generator set.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the generator set fault diagnosis method based on CAE simulation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the generator set fault diagnosis method based on CAE simulation as described in any one of claims 1 to 7 are implemented.

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