Fault Diagnosis Method and System for Generator Sets Based on CAE Simulation

Abstract

The present invention discloses a fault diagnosis method and system for a generator set based on CAE simulation, which relates to the technical field of fault diagnosis. The method includes: performing characteristic analysis and three-dimensional modeling on the structural design information and operating environment information of a hydro-generator set to construct a hydro-generator set model as the basic unit simulation parameters, taking the collected standard generator set operation data stream as the momentum unit simulation parameters, using CAE simulation technology to perform simulation operation on the basic unit simulation parameters and the momentum unit simulation parameters, obtaining a set of unit component simulation parameters for cloud map visualization identification display and fault analysis and diagnosis, and obtaining the fault diagnosis result of the generator set to perform operation and maintenance simulation control on the hydro-generator set. The technical effect of using CAE simulation technology to simulate the operation state of the unit, realizing efficient and accurate diagnosis of unit faults, and improving the applicable range and convenience of unit diagnosis is achieved.

Description

Technical Field

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

[0002] As an important device for hydropower generation, the stability and safety of the operation state of a water turbine generator set are directly related to the reliability and economy of power supply. Due to factors such as the complex structure and harsh operating environment of the generator set, various faults are inevitable during the operation of the unit. However, the existing fault diagnosis methods rely on manual inspections and instrument detections, and there are problems of long diagnosis cycles and low diagnosis accuracy. Summary of the Invention

[0003] This application provides a fault diagnosis method and system for a generator set based on CAE simulation, solves the technical problems of long fault diagnosis cycles and low diagnosis accuracy in the prior art, and achieves the technical effect of using CAE simulation technology to simulate the operation state of the unit, realizing efficient and accurate diagnosis of unit faults, and improving the diagnosis scope and convenience of unit diagnosis.

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

[0005] In a first aspect, this application provides a fault diagnosis method for a generator set based on CAE simulation, and the method includes: S1: Obtain the structural design information and operating environment information of the water turbine generator set, perform characteristic analysis and three-dimensional modeling on the structural design information and operating environment information, and construct a water turbine generator set model; S2: Collect and obtain the generator set operation data stream through the unit monitoring sensor, perform standardized preprocessing on the generator set operation data stream to obtain a standard generator set operation data stream, use the water turbine generator set model as the basic unit simulation parameter, and use the standard generator set operation data stream as the momentum unit simulation parameter; S3: Use CAE simulation technology to perform simulation operation on the basic unit simulation parameter and the momentum unit simulation parameter, obtain a set of unit component simulation parameters for cloud map visualization display; S4: Perform fault analysis and diagnosis on the set of unit component simulation parameters through the generator set safety threshold to obtain a generator set fault diagnosis result, and perform operation and maintenance simulation control on the water turbine generator set according to the generator set fault diagnosis result.

[0006] On the other hand, the present application also provides a fault diagnosis system for a generator set based on CAE simulation. The system includes: a unit model construction module, configured to obtain the structural design information and operating environment information of a 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; a simulation parameter acquisition module, configured to collect and obtain the operation data stream of the generator set through unit monitoring sensors, perform standardized preprocessing on the operation data stream of the generator set to obtain a standard operation data stream of the generator set, use the hydro-generator set model as the basic unit simulation parameter, and use the standard operation data stream of the generator set as the momentum unit simulation parameter; a simulation operation simulation module, configured to use CAE simulation technology to perform simulation operation simulation on the basic unit simulation parameter and the momentum unit simulation parameter, obtain a set of unit component simulation parameters for cloud map visualization display; an operation and maintenance simulation control module, configured to perform fault analysis and diagnosis on the set of unit component simulation parameters 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 hydro-generator set according to the fault diagnosis result of the generator set.

[0007] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0008] In a fourth aspect, 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 steps in any one of the above methods are implemented.

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

[0010] By adopting the method of performing characteristic analysis and three-dimensional modeling on the structural design information and operating environment information of the hydro-generator unit, constructing a hydro-generator unit model, performing standardized preprocessing on the collected operation data stream of the generator unit to obtain a standard operation data stream of the generator unit, using the hydro-generator unit model as the basic unit simulation parameter and the standard operation data stream of the generator unit as the momentum unit simulation parameter, and using CAE simulation technology to perform simulation operation on the basic unit simulation parameter and the momentum unit simulation parameter, obtaining a set of simulation parameters of the unit components for cloud map visualization identification display, and then performing fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generator unit, obtaining a fault diagnosis result of the generator unit for operation and maintenance simulation control of the hydro-generator unit. Furthermore, it achieves the technical effect of using CAE simulation technology to simulate the operation state of the unit, realizing efficient and accurate diagnosis of unit faults, and improving the diagnosis scope and convenience of the unit diagnosis.

[0011] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0012] Figure 1 It is a schematic flowchart of the method for diagnosing faults of a generator unit based on CAE simulation in this application;

[0013] Figure 2 It is a schematic structural diagram of the system for diagnosing faults of a generator unit based on CAE simulation in this application;

[0014] Figure 3 It is a schematic structural diagram of an exemplary electronic device in this application.

[0015] Description of the reference numerals: Unit model construction module 11, simulation parameter acquisition module 12, simulation operation module 13, operation and maintenance simulation control module 14, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application program 1152, and user interface 1160. Detailed Description of the Embodiments

[0016] In the description of the present application, those skilled in the art should understand 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.), and 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, which contains computer program code.

[0017] The above-mentioned computer-readable storage media can 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 components, 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, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, the computer-readable storage media can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0018] The present application describes the provided method, apparatus, and electronic device through flowcharts and / or block diagrams.

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

[0020] These computer-readable program instructions can 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 that includes the instructions for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0021] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing devices, or other devices, so that a series of operation steps are executed on the computer, other programmable data processing devices, or other devices, resulting in a computer-implemented process. Thus, the instructions executed on the computer or other programmable data processing devices can provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0022] The present application will be described below with reference to the accompanying drawings in the present application.

[0023] Embodiment 1

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

[0025] Step S1: Obtain the 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;

[0026] Furthermore, when constructing the hydro-generator set model in S1, the steps of the present application further include:

[0027] S11: Decompose the functional structure of the structural design information and perform attribute marking to obtain a set of attribute characteristic data of the unit components, and perform an analysis of the connection functions of the unit components on the set of attribute characteristic data of the unit components to obtain connection characteristic data information of the unit components;

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

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

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

[0031] Furthermore, when obtaining the set of attribute characteristic data of the unit components in S11, the steps of the present application further include:

[0032] Decompose the functional structure of the structural design information to obtain a set of unit structural components;

[0033] Obtain the attribute element information of the unit components, and the attribute element information of the unit components includes structural dimensions, material properties, and mechanical properties;

[0034] Extract and deepen the content of the attribute element information of the unit components to obtain a set of component attribute content classification nodes, and construct a structural attribute classifier based on the set of component attribute content classification nodes;

[0035] Classify and label the attributes of each component in the set of unit structure components through the structure attribute classifier to obtain the set of unit component attribute characteristic data.

[0036] Specifically, to achieve efficient fault diagnosis of hydro-generator units, first obtain the structural design information of the hydro-generator unit through the turbine design drawings and technical documents, and at the same time monitor and obtain the actual operating environment information of the hydro-generator unit. Then, perform characteristic analysis and 3D modeling on the structural design information and operating environment information. Specifically: disassemble the functional structure and mark the attributes of the structural design information. First, disassemble the unit structure according to the application functions of each component of the unit in the structural design information to obtain the corresponding set of unit structure components, which includes the rotor, stator, frame, brake, thrust bearing, etc. Then determine the information of the attribute elements of the unit components, and the information of the attribute elements of the unit components is the information of the classification indicators of the component attributes, including structural dimensions, material properties, and mechanical characteristics, etc.

[0037] Extract and deepen the content of the information of the attribute elements of the unit components, that is, extract the specific classification content information of the attribute elements of each unit component through the unit component attribute database. Exemplarily, for mechanical characteristics, its specific classification content includes tensile strength, impact resistance strength, etc. Each specific classification content is used as a content classification node to obtain the set of component attribute content classification nodes. And construct a structure attribute classifier based on the set of component attribute content classification nodes. The structure attribute classifier is used to mark the specific parameters of the unit component attributes in sequence according to the content classification nodes. Classify and label the attributes of each component in the set of unit structure components through the structure attribute classifier to obtain the corresponding set of unit component attribute characteristic data. And analyze the component connection methods and interactions of the set of unit component attribute characteristic data through the structural design information, such as shafting connection, bearing support, etc., to obtain the corresponding information on the connection characteristics of the unit components.

[0038] Using three-dimensional modeling technology to perform three-dimensional modeling on the set of unit component attribute characteristic data and the unit component connection characteristic data information, generating a unit component connection model, which is used to display the structural component attributes and connection relationships of the hydro-generator unit. Then, perform multi-dimensional characteristic analysis on the operation environment information to obtain multi-dimensional characteristic parameter information of the operation environment. The multi-dimensional characteristic parameter information of the operation environment is the associated environment parameters affecting the unit operation, including water flow velocity, direction, temperature, humidity, etc. Construct an operation environment characteristic model based on the multi-dimensional characteristic parameter information of the operation environment, which is used to display the operation environment impact information of the hydro-generator unit. Furthermore, obtain the target modeling requirements, which include model accuracy and simulation function requirements. Based on the target modeling requirements, fuse and export the unit component connection model and the operation environment characteristic model to obtain a three-dimensional modeled hydro-generator unit model, which is used as the basis for the subsequent operation simulation model of the hydro-generator unit. Improve the modeling accuracy and comprehensiveness of the hydro-generator unit model, and further improve the subsequent model simulation accuracy.

[0039] Step S2: Collect and obtain the operation data stream of the generator unit through the unit monitoring sensor, perform standardized preprocessing on the operation data stream of the generator unit to obtain the standard operation data stream of the generator unit, use the hydro-generator unit model as the basic unit simulation parameter, and use the standard operation data stream of the generator unit as the momentum unit simulation parameter;

[0040] Specifically, collect and obtain the operation data stream of the hydro-generator unit during actual operation through the unit monitoring sensor. Among them, the unit monitoring sensor is a multi-type sensor group installed on the hydro-generator unit, including flow, temperature, stress, vibration sensors, etc. To ensure the quality of data application, perform standardized preprocessing on the operation data stream of the generator unit, including steps such as filtering or correcting outliers and data normalization processing, to obtain the preprocessed standard operation data stream of the generator unit to ensure data accuracy and consistency. And use the hydro-generator unit model as the basic unit simulation parameter, and use the standard operation data stream of the generator unit as the momentum unit simulation parameter for unit simulation parameter configuration to ensure that the model simulation parameters conform to the actual operation conditions of the unit.

[0041] Step S3: Use CAE simulation technology to perform simulation operation simulation on the basic unit simulation parameter and the momentum unit simulation parameter, and obtain a set of unit component simulation parameters for cloud map visualization identification display;

[0042] Furthermore, for the step of obtaining a set of unit component simulation parameters for cloud map visualization identification display in S3, this application step further includes:

[0043] S31: Use CAE simulation technology to perform simulation modeling and finite element mesh generation on the simulation parameters of the basic unit and the simulation parameters of the momentum unit to generate a generator set simulation model;

[0044] S32: Based on the generator set simulation model, perform operation simulation calculations to obtain the generator set operation simulation results, and extract key parameters from the generator set operation simulation results to obtain the set of simulation parameters for the unit components;

[0045] S33: Obtain the key parameter level division rules according to the generator set application standards, and based on the key parameter level division rules, perform parameter level division on the obtained set of simulation parameters for the unit components to determine the hierarchical distribution information of the simulation parameters for the unit components;

[0046] S34: Use the hierarchical distribution information of the simulation parameters for the unit components to perform contour plot drawing and identification display on the generator set simulation model.

[0047] Specifically, use CAE simulation technology to perform simulation modeling on the simulation parameters of the basic unit and the simulation parameters of the momentum unit, and then perform finite element mesh generation on the simulation model. The density and quality of the mesh directly affect the accuracy of the simulation results, and a generator set simulation model after mesh generation is generated. According to the simulation function requirements, such as generator set operation faults, perform operation simulation calculations 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 fault prediction status of the hydrogenerator set, including operation simulation data streams such as operation stress, temperature, and vibration. And extract key parameters from the generator set operation simulation results to obtain the set of simulation parameters for the unit components. The set of simulation parameters for the unit components is the operation parameter information with a relatively high correlation with the operation status of the generator set, such as parameters like operation stress, temperature, and vibration.

[0048] Obtain the key parameter level division rules according to the generator set application standards. The key parameter level division rules are the parameter level grades divided according to the operation data standards of the hydrogenerator set. Exemplarily, different operation temperature levels are empirically divided according to the operation temperature distribution range of the hydrogenerator set. Based on the key parameter level division rules, perform level division on each simulation parameter in the obtained set of simulation parameters for the unit components to determine the hierarchical distribution information of the simulation parameters for the unit components. The hierarchical distribution information of the simulation parameters for the unit components is the hierarchical distribution information of the simulation parameter operations of each component in the generator set. Furthermore, use the hierarchical distribution information of the simulation parameters for the unit components to perform contour plot drawing and identification display on the generator set simulation model. For example, configure contour plot colors for identification according to the parameter levels, visually display the generator set simulation results, improve the accuracy of the simulation results, and facilitate fault analysis and diagnosis.

[0049] Step S4: 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 intake turbine generator set according to the fault diagnosis result of the generator set.

[0050] Furthermore, for obtaining the fault diagnosis result of the generator set in S4, the steps of this application further include:

[0051] S41: Obtain the fault diagnosis factors of the unit, where the fault diagnosis factors of the unit include fault modes, fault levels, and generation causes. Classify and label the generator set operation fault data set according to the fault diagnosis factors of the unit to obtain the unit operation fault factor data set;

[0052] S42: Use a deep neural network to perform training and equalization fusion on the unit operation fault factor data set to construct a unit operation fault diagnosis model;

[0053] S43: Based on the unit operation fault diagnosis model, perform fault analysis and diagnosis on the set of simulation parameters of the unit components to obtain the fault diagnosis result of the generator set. The fault diagnosis result of the generator set includes a fault diagnosis mode, a fault diagnosis level, and a fault generation cause.

[0054] Furthermore, for performing operation and maintenance simulation control on the water intake turbine generator set according to the fault diagnosis result of the generator set, the steps of this application further include:

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

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

[0057] Perform parameter optimization analysis within the unit operation and maintenance parameter control space to obtain the optimized unit operation and maintenance control parameters, and perform operation and maintenance optimization control on the water intake turbine generator set based on the optimized unit operation and maintenance control parameters.

[0058] Furthermore, the steps of this application further include:

[0059] Perform operation and maintenance iterative simulation on the optimized unit operation and maintenance control parameters 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 mutation direction of the optimization parameters, and set the parameter mutation rule according to the mutation direction of the optimization parameters;

[0061] Fine-tune and optimize the operation and maintenance optimization control parameters of the unit by varying them based on the parameter variation rules.

[0062] Specifically, perform fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generating unit. Among them, the safety threshold of the generating unit is the safe operating parameter range of the water turbine generating unit. First, obtain the fault diagnosis factors of the unit. The fault diagnosis factors of the unit are fault diagnosis evaluation indicators, including fault modes, fault levels, and generation causes. Collect historical fault data through big data, obtain the corresponding operating fault dataset of the generating unit, and classify and label the specific factors of the operating fault dataset of the generating unit according to the fault diagnosis factors. Integrate the dataset according to the types of fault diagnosis factors of the unit to obtain the operating fault factor dataset of the unit. The operating fault factor dataset of the unit includes a fault mode factor dataset, a fault level factor dataset, and a generation cause factor dataset.

[0063] Use a deep neural network to perform fault analysis training on the operating fault factor dataset of the unit respectively until the convergence state is reached, obtain the corresponding fault mode analysis model, fault level analysis model, and generation cause analysis model, and then fuse the fault mode analysis model, fault level analysis model, and generation cause analysis model with equal weights to construct an operating fault diagnosis model of the unit. The operating fault diagnosis model of the unit is used to comprehensively diagnose faults based on the operating parameters of the unit. Perform fault analysis and diagnosis on the set of simulation parameters of the unit components based on the operating fault diagnosis model of the unit, and output the fault diagnosis result of the generating unit. The fault diagnosis result of the generating unit is the operating fault analysis situation of the components of the water turbine generating unit, including the fault diagnosis mode, fault diagnosis level, and fault generation cause. Achieve efficient and accurate diagnosis of unit faults, and improve the diagnosis scope and convenience of the unit diagnosis.

[0064] Perform operation and maintenance simulation control on the hydro-generator set according to the fault diagnosis result of the generator set. First, obtain the operation and maintenance control strategy of the generator set 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 strategy schemes for the hydro-generator set, including improving structural design, optimizing material selection, adjusting working conditions, adjusting operation parameters, etc. Based on the fault diagnosis result of the generator set and the operation and maintenance control strategy of the generator set, perform similarity matching. A similarity algorithm can be used to calculate and screen 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, and determine it as the unit fault operation and maintenance strategy. Further, search for the historical specific operation and maintenance data of the same operation and maintenance strategy according to the unit fault operation and maintenance strategy, and 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 the historical unit fault operation and maintenance control parameters and the corresponding operation and maintenance effect data. Perform parameter optimization analysis within the unit operation and maintenance parameter control space, and compare to obtain the fault operation and maintenance control parameters with the best operation and maintenance effect as the unit operation and maintenance optimization control parameters. And perform operation and maintenance optimization control on the water intake hydro-generator set based on the unit operation and maintenance optimization control parameters to achieve efficient and accurate operation and maintenance analysis of faults, and improve the operation and maintenance effect and operation and maintenance efficiency of the unit.

[0065] To ensure the application effect of the operation and maintenance control parameters, perform operation and maintenance iterative simulation on the unit operation and maintenance optimization control parameters through the generator set simulation model, apply the optimization scheme to the simulation model for analysis, and obtain the operation and maintenance feedback effect of the generator set predicted by the 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 unit operation and maintenance optimization control parameters need to be optimized. Through comparative analysis of the operation and maintenance feedback effect of the generator set, determine the performance of the unit to be optimized that does not reach the preset operation and maintenance effect, and then obtain the variation direction of the optimization parameters through the performance of the unit to be optimized. The variation direction of the optimization parameters is the type of associated operation and maintenance control parameters related to the performance of the unit to be optimized in the unit operation and maintenance optimization control parameters. And set the parameter variation rule according to the variation direction of the optimization parameters. The parameter variation rule is the basis for adjusting and varying the parameter values of the type of associated operation and maintenance control parameters. For example, fine-tune and vary the parameter values according to the normal distribution. Based on the parameter variation rule, perform variation fine-tuning optimization on the unit operation and maintenance optimization control parameters, obtain multiple optimized unit operation and maintenance optimization control parameters, and then perform operation and maintenance effect optimization selection on the multiple unit operation and maintenance optimization control parameters. Improve the analysis accuracy and practical applicability of the unit operation and maintenance control parameters, and thus ensure the operation and maintenance effect of the generator set.

[0066] In summary, the method for diagnosing faults in a generator set based on CAE simulation provided by this application has the following technical effects:

[0067] Due to the adoption of characteristic analysis and 3D modeling of the structural design information and operating environment information of the hydro-generating unit, a hydro-generating unit model is constructed. The operating data stream of the generating unit collected is preprocessed in a standardized manner to obtain a standard operating data stream of the generating unit. The hydro-generating unit model is used as the basic unit simulation parameter, and the standard operating data stream of the generating unit is used as the momentum unit simulation parameter. The CAE simulation technology is used to simulate the operation of the basic unit simulation parameter and the momentum unit simulation parameter, and a set of simulation parameters of the unit components is obtained for cloud map visualization identification display. Furthermore, the fault analysis and diagnosis of the set of simulation parameters of the unit components are carried out through the safety threshold of the generating unit, and the fault diagnosis result of the generating unit is obtained to perform operation and maintenance simulation control on the hydro-generating unit. Furthermore, the technical effect of using the CAE simulation technology to simulate the operation state of the unit, realizing the efficient and accurate diagnosis of the unit fault, and improving the diagnosis scope and convenience of the unit diagnosis is achieved.

[0068] Embodiment 2

[0069] Based on the same inventive concept as the method for diagnosing faults in a generating unit based on CAE simulation in the foregoing embodiment, the present invention also provides a system for diagnosing faults in a generating unit based on CAE simulation, as Figure 2 shown, the system includes:

[0070] A unit model construction module 11, configured to obtain the structural design information and operating environment information of the hydro-generating unit, perform characteristic analysis and 3D modeling on the structural design information and operating environment information, and construct a hydro-generating unit model;

[0071] A simulation parameter acquisition module 12, configured to collect and obtain the operating data stream of the generating unit through a unit monitoring sensor, preprocess the operating data stream of the generating unit in a standardized manner to obtain a standard operating data stream of the generating unit, use the hydro-generating unit model as the basic unit simulation parameter, and use the standard operating data stream of the generating unit as the momentum unit simulation parameter;

[0072] A simulation operation simulation module 13, configured to use the CAE simulation technology to perform simulation operation on the basic unit simulation parameter and the momentum unit simulation parameter, and obtain a set of simulation parameters of the unit components for cloud map visualization identification display;

[0073] An operation and maintenance simulation control module 14, configured to perform fault analysis and diagnosis on the set of simulation parameters of the unit components through the safety threshold of the generating unit, obtain the fault diagnosis result of the generating unit, and perform operation and maintenance simulation control on the hydro-generating unit according to the fault diagnosis result of the generating unit.

[0074] Furthermore, the unit model construction module 11 is further configured to:

[0075] Functionally decompose the structural design information and perform attribute marking to obtain a set of data on the attribute characteristics of the unit components, and analyze the connection effects of the unit components on the set of data on the attribute characteristics of the unit components to obtain information on the connection characteristics of the unit components;

[0076] Use 3D modeling technology to perform solid modeling on the set of data on the attribute characteristics of the unit components and the information on the connection characteristics of the unit components to generate a connection model of the unit components;

[0077] Perform multi-dimensional characteristic analysis on the operating environment information to obtain multi-dimensional characteristic parameter information of the operating environment, and construct an operating environment characteristic model based on the multi-dimensional characteristic parameter information of the operating environment;

[0078] Obtain the target modeling requirements, and based on the target modeling requirements, fuse and export the connection model of the unit components and the operating environment characteristic model to construct the water turbine generator unit model.

[0079] Further, the unit model construction module 11 is also used for:

[0080] Functionally decompose the structural design information to obtain a set of unit structural components;

[0081] Obtain information on the attribute elements of the unit components, where the information on the attribute elements of the unit components includes structural dimensions, material properties, and mechanical characteristics;

[0082] Extract and deepen the content of the information on the attribute elements of the unit components to obtain a set of component attribute content classification nodes, and construct a structural attribute classifier based on the set of component attribute content classification nodes;

[0083] Use the structural attribute classifier to perform attribute classification and marking on each component in the set of unit structural components to obtain the set of data on the attribute characteristics of the unit components.

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

[0085] Use CAE simulation technology to perform simulation modeling and finite element mesh division on the basic unit simulation parameters and the momentum unit simulation parameters to generate a generator set simulation model;

[0086] Perform operation simulation calculations based on the generator set simulation model to obtain the operation simulation results of the generator set, and extract key parameters from the operation simulation results of the generator set to obtain the set of simulation parameters of the unit components;

[0087] Obtain the hierarchical division rule of key parameters according to the application standard of the generating unit, and perform parameter hierarchical division on the obtained set of simulation parameters of the unit components based on the hierarchical division rule of key parameters to determine the hierarchical distribution information of the simulation parameters of the unit components;

[0088] Use the hierarchical distribution information of the simulation parameters of the unit components to draw and display the contour map of the simulation model of the generating unit.

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

[0090] Obtain the fault diagnosis factors of the generating unit, where the fault diagnosis factors of the generating unit include fault modes, fault levels, and generation causes, and classify and label the operation fault data set of the generating unit according to the fault diagnosis factors of the generating unit to obtain the operation fault factor data set of the generating unit;

[0091] Use a deep neural network to perform training and equalization fusion on the operation fault factor data set of the generating unit to construct an operation fault diagnosis model of the generating unit;

[0092] Based on the operation fault diagnosis model of the generating unit, perform fault analysis and diagnosis on the set of simulation parameters of the unit components to obtain the fault diagnosis result of the generating unit, where the fault diagnosis result of the generating unit includes fault diagnosis mode, fault diagnosis level, and fault generation cause.

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

[0094] Obtain the operation and maintenance control strategy of the generating unit, perform similarity matching based on the fault diagnosis result of the generating unit and the operation and maintenance control strategy of the generating unit to determine the fault operation and maintenance strategy of the unit;

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

[0096] Perform parameter optimization analysis within the control space of the operation and maintenance parameters of the unit to obtain the optimized control parameters for the operation and maintenance of the unit, and perform operation and maintenance optimization control on the water intake turbine generator set based on the optimized control parameters for the operation and maintenance of the unit.

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

[0098] Perform operation and maintenance iterative simulation on the optimized control parameters for the operation and maintenance of the unit through the simulation model of the generating unit to obtain the operation and maintenance feedback effect of the generating unit;

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

[0100] Based on the parameter mutation rule, the unit operation and maintenance optimization control parameters are mutated and finely tuned and optimized.

[0101] The foregoing Figure 1 All the various change modes and specific examples of the generator set fault diagnosis method based on CAE simulation in the first embodiment are equally applicable to the generator set fault diagnosis system based on CAE simulation in this embodiment. Through the foregoing 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 herein.

[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 on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it implements each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0103] Exemplary electronic device

[0104] Specifically, referring to 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 on the memory 1150 and executable on the processor 1120. When the computer program is executed by the processor 1120, it implements each process of the method embodiment for controlling the output data.

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

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

[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. By way of example, and not limitation, such architectures include: Industry Standard Architecture bus, Micro Channel Architecture bus, Extended bus, Video Electronics Standards Association, Peripheral Component Interconnect bus.

[0109] Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be completed by the integrated logic circuit in the processor or instructions in software form. The above processors include: general-purpose processors, central processors, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, microcontroller units, or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the present 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 on 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 connection with the present application may be directly executed by a hardware decoding processor or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium well known in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, registers, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

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

[0112] The transceiver 1130 may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send the 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: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.

[0113] It should be understood that in this application, the memory 1150 may further include memories remotely located relative to the processor 1120, and these remotely located memories can be connected to the server through a network. One or more parts of the above network can be an ad hoc 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 Wi-Fi network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network can be a global mobile communication device, a code division multiple access device, a worldwide interoperability for microwave access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, an LTE frequency division duplex device, an LTE time division duplex device, an advanced long term evolution device, a universal mobile telecommunications system device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, etc.

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

[0115] The 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, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in this application includes but is not limited to the above and any other suitable types of memory.

[0116] In this 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 contains various device programs, such as: a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 1152 contains various application programs, such as: a media player, a browser, for implementing various application services. The program for implementing the method of this application can be included in the application program 1152. The application program 1152 includes: applets, objects, components, logics, data structures, and other computer device executable instructions for performing specific tasks or implementing 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, each process of the above method embodiment for controlling the output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be elaborated here.

[0119] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to 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, obtaining a fault diagnosis result of the generator set, and performing operation and maintenance simulation control on the water turbine generator set according to the fault diagnosis result of the generator set; 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: Obtaining target modeling requirements, fusing and exporting the unit component connection model and the operating environment characteristic model based on the target modeling requirements, and constructing the hydro-generator unit model; 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.

2. The method according to claim 1, 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.

3. 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.

4. The method according to claim 1, 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.

5. The method according to claim 4, 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.

6. 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 5, 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.

7. 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 5 are implemented.

8. 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 5 are implemented.

Images