A fault simulation method and apparatus based on digital twins

Abstract

The present application provides a fault simulation method and device based on digital twins. The method includes: constructing a digital twin model of industrial equipment and a fault simulator, performing fault simulation according to the digital twin model, processing according to the fault simulation result data, obtaining a fault performance evaluation index, obtaining a fault evaluation influence factor according to the historical fault record data processing, and then obtaining a fault performance evaluation correction index, determining the fault type of focus according to the fault performance evaluation correction index, and then processing to obtain a fault prediction model corresponding to the fault type of focus, processing according to the fault prediction model to obtain the fault prediction result data and push it to the user, and processing according to the fault performance evaluation index to obtain a comprehensive fault performance evaluation index, and performing reliability evaluation on the equipment according to the comprehensive fault performance evaluation index. Thereby achieving the purpose of fault prediction and reliability evaluation of the equipment through fault simulation technology based on digital twins.

Description

Technical Field

[0001] The present application relates to the field of big data and fault simulation technology, and specifically, to a fault simulation method and device based on digital twins. Background Art

[0002] In industrial scenarios, it is difficult to collect data on equipment failures, so in most data samples arranged in time series, equipment failure data only occupies a very small part, which is a big obstacle for industrial data analysis. In traditional fault simulation methods, fault injection and testing often rely on actual physical equipment. This method has many disadvantages, such as possible damage to the equipment, high testing costs, limited testing scenarios, and difficulty in reproducing complex fault conditions. Moreover, once a fault test is performed on the actual equipment, it may affect normal production or operation, causing unnecessary losses. Using digital twin technology for fault simulation can simulate the working state of the equipment under different fault conditions, and accurately simulate various fault conditions without affecting the normal operation of the actual equipment. However, there is currently a lack of a technology that analyzes the potential faults of equipment and predicts faults based on the response of industrial equipment to different simulated faults.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of the present application is to provide a fault simulation method and device based on digital twins, which obtains a fault performance evaluation index according to the fault simulation result data of the constructed digital twin model, and corrects it to determine the key fault type, and then processes it to obtain a fault prediction model corresponding to the key fault type, predicts the key fault according to the fault prediction model, and then evaluates the reliability of the equipment, thereby achieving the purpose of fault prediction and reliability evaluation of the equipment through fault simulation technology based on digital twins.

[0005] The present application also provides a fault simulation method based on digital twins, comprising the following steps:

[0006] Build digital twin models of industrial equipment and fault simulators, perform fault simulation based on the digital twin models, and obtain fault simulation result data;

[0007] Processing the fault simulation result data to obtain a fault performance evaluation index;

[0008] Obtain historical fault record data of industrial equipment, and obtain fault assessment impact factors based on historical fault record data processing;

[0009] Processing is performed according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and determining a key fault type according to the fault performance evaluation correction index;

[0010] A fault prediction model corresponding to a focused fault type is obtained based on the historical fault record data processing, and fault prediction result data is obtained based on the fault prediction model processing and pushed to the user.

[0011] Optionally, in the digital twin-based fault simulation method described in the present application, the digital twin model of the industrial equipment and the fault simulator is constructed, and the fault simulation is performed according to the digital twin model to obtain the fault simulation result data, including:

[0012] Acquire equipment design parameter data, equipment operation parameter data and equipment working environment data of industrial equipment, and acquire simulator design parameter data and simulator working parameter data of a fault simulator;

[0013] Constructing a digital twin model based on the device design parameter data, the device operation parameter data, the device working environment data, the simulator design parameter data, and the simulator working parameter data using a preset modeling technology;

[0014] Performing fault simulation according to the digital twin model to obtain fault simulation result data, including mechanical fault simulation result data, electrical fault simulation result data, and control fault simulation result data;

[0015] The mechanical fault simulation result data includes abnormal noise data, mechanical fault operation smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data;

[0016] The electrical fault simulation result data includes motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data;

[0017] The control fault simulation result data includes control fault running smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data.

[0018] Optionally, in the digital twin-based fault simulation method described in the present application, the processing according to the fault simulation result data to obtain a fault performance evaluation index includes:

[0019] Calculate according to the abnormal noise data, mechanical fault running smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data to obtain a mechanical fault performance evaluation index;

[0020] Calculating according to the motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data to obtain an electrical fault performance evaluation index;

[0021] A control fault performance evaluation index is obtained by performing calculations based on the control fault running smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data.

[0022] Optionally, in the digital twin-based fault simulation method described in the present application, the acquiring of historical fault record data of industrial equipment and obtaining a fault assessment impact factor according to the processing of the historical fault record data include:

[0023] Obtain historical fault record data of industrial equipment, and extract fault impact degree data and fault occurrence frequency data corresponding to mechanical faults, electrical faults and control faults respectively according to the historical fault record data;

[0024] The fault impact degree data includes production efficiency impact degree data, product quality impact degree data and economic loss degree data;

[0025] Calculation is performed based on the production efficiency impact data, product quality impact data, economic loss degree data and the fault frequency data to obtain fault assessment impact factors, including mechanical fault assessment impact factors, electrical fault assessment impact factors and control fault assessment impact factors.

[0026] Optionally, in the digital twin-based fault simulation method described in the present application, the processing is performed according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and the key fault type is determined according to the fault performance evaluation correction index, including:

[0027] According to the mechanical fault evaluation influence factor, the electrical fault evaluation influence factor and the control fault evaluation influence factor, the mechanical fault performance evaluation index, the electrical fault performance evaluation index and the control fault performance evaluation index are respectively corrected to obtain the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index respectively;

[0028] The mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index are sorted according to their numerical values, and the fault type corresponding to the performance evaluation correction index with the largest numerical value is taken as the key focus fault type.

[0029] Optionally, in the digital twin-based fault simulation method described in the present application, the processing of the historical fault record data to obtain a fault prediction model corresponding to the focused fault type, and the processing of the fault prediction model to obtain fault prediction result data and push it to the user include:

[0030] Extracting, based on the historical fault record data, the fault occurrence time data, fault mode data, fault maintenance operation data corresponding to the fault and the corresponding equipment historical operation parameter data and equipment historical operation environment data;

[0031] Training is performed based on the fault occurrence time data, fault mode data, fault maintenance operation data and their corresponding equipment historical operating parameter data and equipment historical operating environment data to obtain a fault prediction model;

[0032] The equipment operation parameter data and equipment working environment data of the industrial equipment are input into the fault prediction model for processing, and the fault prediction result data is obtained and pushed to the user.

[0033] Optionally, the digital twin-based fault simulation method described in the present application further includes:

[0034] Calculate according to the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index to obtain a comprehensive fault performance evaluation index;

[0035] The fault comprehensive performance evaluation index is compared with a preset fault comprehensive performance evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, the industrial equipment is judged to be unreliable.

[0036] In a second aspect, the present application provides a fault simulation device based on digital twins, comprising:

[0037] A data acquisition module is used to collect equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data of industrial equipment, and simulator design parameter data and simulator working parameter data of a fault simulator;

[0038] A data processing module is used to process the collected equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data, and simulator design parameter data and simulator working parameter data of the fault simulator to obtain a digital twin model, fault simulation result data, fault performance evaluation index, fault evaluation influence factor, fault performance evaluation correction index, fault prediction result data, and fault comprehensive performance evaluation index;

[0039] The judgment and evaluation module is used to determine the type of fault to be focused on based on the fault performance evaluation correction index, and to make reliability judgments on industrial equipment based on the comprehensive fault performance evaluation index.

[0040] Optionally, in the fault simulation device based on digital twin described in the present application, the data processing module is used to:

[0041] Building a digital twin model based on the equipment design parameter data, equipment operation parameter data, equipment working environment data, and the simulator design parameter data and simulator working parameter data using a preset modeling technology, performing fault simulation based on the digital twin model, and obtaining fault simulation result data;

[0042] Processing the fault simulation result data to obtain a fault performance evaluation index;

[0043] Obtaining a fault assessment impact factor according to the historical fault record data processing;

[0044] Processing is performed according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index;

[0045] Obtain a fault prediction model corresponding to the focused fault type according to the historical fault record data processing, obtain fault prediction result data according to the fault prediction model processing and push it to the user;

[0046] The comprehensive fault performance evaluation index is obtained by calculation based on the mechanical fault evaluation influence factor, electrical fault evaluation influence factor and control fault evaluation influence factor as well as the mechanical fault performance evaluation index, electrical fault performance evaluation index and control fault performance evaluation index.

[0047] From the above, it can be seen that the present application provides a digital twin-based fault simulation method and device, which obtains a fault performance evaluation index according to the fault simulation result data of the constructed digital twin model, and corrects it to determine the key fault type, and then processes it to obtain the fault prediction model corresponding to the key fault type, and predicts the key fault according to the fault prediction model and then evaluates the reliability of the equipment, thereby achieving the purpose of fault prediction and reliability evaluation of the equipment through fault simulation technology based on digital twin.

[0048] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A flowchart of a fault simulation method based on digital twins provided in an embodiment of the present application;

[0051] Figure 2 A flowchart of obtaining fault simulation result data of a fault simulation method based on digital twins provided in an embodiment of the present application;

[0052] Figure 3 A flowchart of obtaining a fault performance evaluation index for a fault simulation method based on digital twins provided in an embodiment of the present application;

[0053] Figure 4 A flow chart of obtaining a fault assessment impact factor for a digital twin-based fault simulation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0055] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0056] Please refer to Figure 1 , Figure 1 : is a flow chart of a fault simulation method based on digital twins in some embodiments of the present application. The fault simulation method based on digital twins is used in terminal devices, such as computers, mobile phone terminals, etc. The fault simulation method based on digital twins includes the following steps:

[0057] S11. Build a digital twin model of industrial equipment and a fault simulator, perform fault simulation according to the digital twin model, and obtain fault simulation result data;

[0058] S12, processing the fault simulation result data to obtain a fault performance evaluation index;

[0059] S13, obtaining historical fault record data of industrial equipment, and obtaining a fault assessment impact factor according to the historical fault record data;

[0060] S14, performing processing according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and determining a key fault type according to the fault performance evaluation correction index;

[0061] S15. Obtain a fault prediction model corresponding to the focused fault type according to the historical fault record data, obtain fault prediction result data according to the fault prediction model, and push it to the user.

[0062] It should be noted that the fault performance evaluation index is obtained by processing the fault simulation result data of the constructed digital twin model, and correction is performed to determine the key fault type, and then the fault prediction model corresponding to the key fault type is obtained. The key fault is predicted according to the fault prediction model and then the reliability of the equipment is evaluated, thereby achieving the purpose of fault prediction and reliability evaluation of the equipment through fault simulation technology based on digital twins.

[0063] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of a fault simulation method based on digital twins for obtaining fault simulation result data in some embodiments of the present invention. According to an embodiment of the present invention, the digital twin model of industrial equipment and fault simulator is constructed, and fault simulation is performed according to the digital twin model to obtain fault simulation result data, including:

[0064] S21, obtaining equipment design parameter data, equipment operation parameter data and equipment working environment data of industrial equipment, and obtaining simulator design parameter data and simulator working parameter data of a fault simulator;

[0065] S22, constructing a digital twin model according to the device design parameter data, the device operation parameter data, the device working environment data, the simulator design parameter data and the simulator working parameter data using a preset modeling technology;

[0066] S23. Perform fault simulation according to the digital twin model to obtain fault simulation result data, including mechanical fault simulation result data, electrical fault simulation result data, and control fault simulation result data;

[0067] S24, the mechanical fault simulation result data includes abnormal noise data, mechanical fault operation smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data;

[0068] S25, the electrical fault simulation result data includes motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data;

[0069] S26. The control fault simulation result data includes control fault operation smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data.

[0070] It should be noted that first, a digital twin model of industrial equipment and a fault simulator is constructed, and then mechanical failure, electrical failure and control failure simulation are carried out. When the equipment has mechanical failures such as bearing wear, poor gear meshing, uneven guide rails, etc., abnormal noise, unstable movement, reduced load capacity, reduced positioning accuracy of the motion module, and large positioning deviations each time when positioning is repeated multiple times often occur. When electrical failures such as power failures and motor drive circuit failures occur, motor operation instability, abnormal output of electrical parameters such as voltage and current, reduced positioning accuracy of the motion module, and large positioning deviations each time when positioning is repeated multiple times often occur. When a control failure occurs, there are often problems such as unstable operation, delayed response, abnormal instruction execution, reduced positioning accuracy of the motion module, and large positioning deviations each time when positioning is repeated multiple times.

[0071] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the fault performance evaluation index of the fault simulation method based on digital twin in some embodiments of the present application. According to the embodiment of the present invention, the processing according to the fault simulation result data to obtain the fault performance evaluation index includes:

[0072] S31, calculating according to the abnormal noise data, mechanical fault running smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data to obtain a mechanical fault performance evaluation index;

[0073] S32, calculating according to the motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data to obtain an electrical fault performance evaluation index;

[0074] S33, performing calculations based on the control fault operation smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data to obtain a control fault performance evaluation index.

[0075] It should be noted that the equipment performance is evaluated by analyzing the equipment performance when mechanical failure, electrical failure and control failure occur, and the mechanical failure performance evaluation index, electrical failure performance evaluation index and control failure performance evaluation index are obtained;

[0076] The calculation formula of the mechanical failure performance evaluation index is:

[0077] ;

[0078] in, is the mechanical failure performance evaluation index, is abnormal noise data, This is the running stability data of mechanical failure. is the mechanical positioning deviation data, is the mechanical positioning deviation stability data, is the load capacity deviation data, , , and is a preset characteristic coefficient (which can be obtained by querying the preset digital twin fault simulation database);

[0079] The calculation formula of the electrical fault performance evaluation index is:

[0080] ;

[0081] in, is the electrical fault performance evaluation index, is the motor operation stability data, Output data for abnormal electrical parameters. is the electrical positioning deviation data, is the electrical positioning deviation stability data, , , and is a preset characteristic coefficient (which can be obtained by querying the preset digital twin fault simulation database);

[0082] The calculation formula of the control fault performance evaluation index is:

[0083] ;

[0084] in, To control the fault performance evaluation index, To control the fault running smoothness data, To control the positioning deviation data, To control the positioning deviation stability data, In response to delayed data, For instruction execution accuracy data, , , , and is the preset characteristic coefficient (which can be obtained by querying the preset digital twin fault simulation database).

[0085] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a fault assessment impact factor of a fault simulation method based on digital twins in some embodiments of the present application. According to an embodiment of the present invention, the method of obtaining historical fault record data of industrial equipment and obtaining a fault assessment impact factor based on the historical fault record data includes:

[0086] S41, obtaining historical fault record data of industrial equipment, and extracting fault impact degree data and fault occurrence frequency data corresponding to mechanical faults, electrical faults and control faults respectively according to the historical fault record data;

[0087] S42, the fault impact degree data includes production efficiency impact degree data, product quality impact degree data and economic loss degree data;

[0088] S43. Calculate according to the production efficiency impact data, product quality impact data, economic loss degree data and the fault frequency data to obtain fault assessment impact factors, including mechanical fault assessment impact factors, electrical fault assessment impact factors and control fault assessment impact factors.

[0089] It should be noted that the fault assessment impact factor is obtained by calculating based on the fault impact degree data and the fault frequency data;

[0090] The calculation formula of the fault evaluation influence factor is:

[0091] ;

[0092] Where i=1,2,3, They are mechanical fault assessment impact factor, electrical fault assessment impact factor, and control fault assessment impact factor. They are the fault frequency data corresponding to mechanical faults, electrical faults and control faults, These are the production efficiency impact data corresponding to mechanical failure, electrical failure and control failure. They are the product quality impact data corresponding to mechanical failure, electrical failure and control failure. These are the economic loss data corresponding to mechanical failure, electrical failure and control failure. is the preset characteristic coefficient corresponding to the mechanical fault (which can be obtained by querying the preset digital twin fault simulation database), is the preset characteristic coefficient corresponding to the electrical fault (which can be obtained by querying the preset digital twin fault simulation database), It is the preset characteristic coefficient corresponding to the control fault (which can be obtained by querying the preset digital twin fault simulation database).

[0093] According to an embodiment of the present invention, the processing according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and determining the fault type of focus according to the fault performance evaluation correction index includes:

[0094] According to the mechanical fault evaluation influence factor, the electrical fault evaluation influence factor and the control fault evaluation influence factor, the mechanical fault performance evaluation index, the electrical fault performance evaluation index and the control fault performance evaluation index are respectively corrected to obtain the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index respectively;

[0095] The mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index are sorted according to their numerical values, and the fault type corresponding to the performance evaluation correction index with the largest numerical value is taken as the key focus fault type.

[0096] It should be noted that the fault performance evaluation correction index is obtained by processing the fault evaluation influence factor and the fault performance evaluation index, and the fault type to be focused on is determined according to the fault performance evaluation correction index;

[0097] The calculation formulas for the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index are as follows:

[0098] ;

[0099] Where i=1,2,3, They are the mechanical fault performance evaluation correction index, electrical fault performance evaluation correction index and control fault performance evaluation correction index. They are mechanical fault assessment impact factor, electrical fault assessment impact factor and control fault assessment impact factor. They are mechanical fault performance evaluation index, electrical fault performance evaluation index and control fault performance evaluation index. They are the preset characteristic coefficients corresponding to the mechanical fault performance evaluation index, the electrical fault performance evaluation index and the control fault performance evaluation index respectively (which can be obtained by querying the preset digital twin fault simulation database).

[0100] According to an embodiment of the present invention, the processing of the historical fault record data to obtain a fault prediction model corresponding to the focused fault type, and the processing of the fault prediction model to obtain fault prediction result data and push it to the user include:

[0101] Extracting, based on the historical fault record data, the fault occurrence time data, fault mode data, fault maintenance operation data corresponding to the fault and the corresponding equipment historical operation parameter data and equipment historical operation environment data;

[0102] Training is performed based on the fault occurrence time data, fault mode data, fault maintenance operation data and their corresponding equipment historical operating parameter data and equipment historical operating environment data to obtain a fault prediction model;

[0103] The equipment operation parameter data and equipment working environment data of the industrial equipment are input into the fault prediction model for processing, and the fault prediction result data is obtained and pushed to the user.

[0104] It should be noted that the fault prediction model corresponding to the focused fault type is obtained based on the processing of historical fault record data, and the fault prediction result data is obtained based on the fault prediction model and pushed to the user. The preset fault prediction model is a model obtained by training by acquiring a large number of historical samples of fault occurrence time data, fault mode data, fault maintenance operation data and its corresponding equipment historical operating parameter data, equipment historical operating environment data and fault prediction result data. The model can be processed by inputting relevant data to obtain the corresponding output fault prediction result data.

[0105] According to an embodiment of the present invention, it also includes:

[0106] Calculate according to the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index to obtain a comprehensive fault performance evaluation index;

[0107] The fault comprehensive performance evaluation index is compared with a preset fault comprehensive performance evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, the industrial equipment is judged to be unreliable.

[0108] It should be noted that the overall performance of the equipment when a failure occurs is evaluated based on the performance evaluation of industrial equipment for mechanical failure, electrical failure, and control failure;

[0109] The calculation formula of the comprehensive fault performance evaluation index is:

[0110] ;

[0111] Where i=1,2,3, is the comprehensive performance evaluation index of faults, They are the mechanical fault performance evaluation correction index, electrical fault performance evaluation correction index and control fault performance evaluation correction index. They are the preset characteristic coefficients corresponding to the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index respectively (which can be obtained by querying the preset digital twin fault simulation database).

[0112] The present invention also discloses a fault simulation device based on digital twin, comprising:

[0113] A data acquisition module is used to collect equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data of industrial equipment, and simulator design parameter data and simulator working parameter data of a fault simulator;

[0114] A data processing module is used to process the collected equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data, and simulator design parameter data and simulator working parameter data of the fault simulator to obtain a digital twin model, fault simulation result data, fault performance evaluation index, fault evaluation influence factor, fault performance evaluation correction index, fault prediction result data, and fault comprehensive performance evaluation index;

[0115] The judgment and evaluation module is used to determine the type of fault to be focused on based on the fault performance evaluation correction index, and to make reliability judgments on industrial equipment based on the comprehensive fault performance evaluation index.

[0116] It should be noted that the fault simulation device obtains equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data and simulator design parameter data and simulator working parameter data of the fault simulator through the data acquisition module; the data processing module processes the collected equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data and simulator design parameter data and simulator working parameter data of the fault simulator to obtain a digital twin model, fault simulation result data, fault performance evaluation index, fault evaluation influence factor, fault performance evaluation correction index, fault prediction result data and comprehensive fault performance evaluation index; the judgment and evaluation module determines the type of fault to focus on based on the fault performance evaluation correction index, and makes a reliability judgment on the industrial equipment based on the comprehensive fault performance evaluation index.

[0117] According to an embodiment of the present invention, the data processing module is used to:

[0118] Building a digital twin model based on the equipment design parameter data, equipment operation parameter data, equipment working environment data, and the simulator design parameter data and simulator working parameter data using a preset modeling technology, performing fault simulation based on the digital twin model, and obtaining fault simulation result data;

[0119] Processing the fault simulation result data to obtain a fault performance evaluation index;

[0120] Obtaining a fault assessment impact factor according to the historical fault record data processing;

[0121] Processing is performed according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index;

[0122] Obtain a fault prediction model corresponding to the focused fault type according to the historical fault record data processing, obtain fault prediction result data according to the fault prediction model processing and push it to the user;

[0123] The comprehensive fault performance evaluation index is obtained by calculation based on the mechanical fault evaluation influence factor, electrical fault evaluation influence factor and control fault evaluation influence factor as well as the mechanical fault performance evaluation index, electrical fault performance evaluation index and control fault performance evaluation index.

[0124] It should be noted that the data processing module processes the collected equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data, and simulator design parameter data and simulator working parameter data of the fault simulator to obtain a digital twin model, fault simulation result data, fault performance evaluation index, fault evaluation influence factor, fault performance evaluation correction index, fault prediction result data, and fault comprehensive performance evaluation index.

[0125] The present invention discloses a fault simulation method and device based on digital twins. The fault performance evaluation index is obtained by processing the fault simulation result data of the constructed digital twin model, and correction is performed to determine the key fault type. The fault prediction model corresponding to the key fault type is then processed to predict the key fault according to the fault prediction model and then the reliability of the equipment is evaluated, thereby achieving the purpose of fault prediction and reliability evaluation of the equipment through fault simulation technology based on digital twins.

[0126] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0127] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0128] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0129] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps including the above method embodiment.

[0130] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A fault simulation method based on digital twins, characterized in that: The following steps are involved: Build digital twin models of industrial equipment and fault simulators, perform fault simulation based on the digital twin models, and obtain fault simulation result data; Processing the fault simulation result data to obtain a fault performance evaluation index; Obtain historical fault record data of industrial equipment, and obtain fault assessment impact factors based on historical fault record data processing; Processing is performed according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and determining a key fault type according to the fault performance evaluation correction index; Obtain a fault prediction model corresponding to the focused fault type according to the historical fault record data processing, obtain fault prediction result data according to the fault prediction model processing and push it to the user; The digital twin model of the industrial equipment and the fault simulator is constructed, and the fault simulation is performed according to the digital twin model to obtain the fault simulation result data, including: Acquire equipment design parameter data, equipment operation parameter data and equipment working environment data of industrial equipment, and acquire simulator design parameter data and simulator working parameter data of a fault simulator; Constructing a digital twin model based on the device design parameter data, the device operation parameter data, the device working environment data, the simulator design parameter data, and the simulator working parameter data using a preset modeling technology; Performing fault simulation according to the digital twin model to obtain fault simulation result data, including mechanical fault simulation result data, electrical fault simulation result data, and control fault simulation result data; The mechanical fault simulation result data includes abnormal noise data, mechanical fault operation smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data; The electrical fault simulation result data includes motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data; The control fault simulation result data includes control fault operation smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data; The processing according to the fault simulation result data to obtain the fault performance evaluation index includes: Calculate according to the abnormal noise data, mechanical fault running smoothness data, mechanical positioning deviation data, mechanical positioning deviation stability data and load capacity deviation data to obtain a mechanical fault performance evaluation index; Calculating according to the motor operation stability data, electrical parameter abnormal output data, electrical positioning deviation data and electrical positioning deviation stability data to obtain an electrical fault performance evaluation index; Calculate according to the control fault running smoothness data, control positioning deviation data, control positioning deviation stability data, response delay data and instruction execution accuracy data to obtain a control fault performance evaluation index; The method of obtaining historical fault record data of industrial equipment and obtaining a fault assessment impact factor according to the historical fault record data includes: Obtain historical fault record data of industrial equipment, and extract fault impact degree data and fault occurrence frequency data corresponding to mechanical faults, electrical faults and control faults respectively according to the historical fault record data; The fault impact degree data includes production efficiency impact degree data, product quality impact degree data and economic loss degree data; Calculation is performed based on the production efficiency impact data, product quality impact data, economic loss degree data and the fault frequency data to obtain fault assessment impact factors, including mechanical fault assessment impact factors, electrical fault assessment impact factors and control fault assessment impact factors.

2. The fault simulation method based on digital twin according to claim 1, characterized in that: The processing according to the fault evaluation influence factor and the fault performance evaluation index to obtain a fault performance evaluation correction index, and determining the fault type to be focused on according to the fault performance evaluation correction index, includes: According to the mechanical fault evaluation influence factor, the electrical fault evaluation influence factor and the control fault evaluation influence factor, the mechanical fault performance evaluation index, the electrical fault performance evaluation index and the control fault performance evaluation index are respectively corrected to obtain the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index respectively; The mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index are sorted according to their numerical values, and the fault type corresponding to the performance evaluation correction index with the largest numerical value is taken as the key focus fault type.

3. The fault simulation method based on digital twin according to claim 2, characterized in that: The processing of the historical fault record data to obtain a fault prediction model corresponding to the focused fault type, and the processing of the fault prediction model to obtain fault prediction result data and push it to the user include: Extracting, based on the historical fault record data, the fault occurrence time data, fault mode data, fault maintenance operation data corresponding to the fault and the corresponding equipment historical operation parameter data and equipment historical operation environment data; Training is performed based on the fault occurrence time data, fault mode data, fault maintenance operation data and their corresponding equipment historical operating parameter data and equipment historical operating environment data to obtain a fault prediction model; The equipment operation parameter data and equipment working environment data of the industrial equipment are input into the fault prediction model for processing, and the fault prediction result data is obtained and pushed to the user.

4. The fault simulation method based on digital twin according to claim 3 is characterized in that: Also includes: Calculate according to the mechanical fault performance evaluation correction index, the electrical fault performance evaluation correction index and the control fault performance evaluation correction index to obtain a comprehensive fault performance evaluation index; The fault comprehensive performance evaluation index is compared with a preset fault comprehensive performance evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, the industrial equipment is judged to be unreliable.

5. A fault simulation device based on digital twin, used to implement the fault simulation method based on digital twin according to any one of claims 1 to 4, characterized in that: include: A data acquisition module is used to collect equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data of industrial equipment, and simulator design parameter data and simulator working parameter data of a fault simulator; A data processing module is used to process the collected equipment design parameter data, equipment operation parameter data, equipment working environment data, historical fault record data, and simulator design parameter data and simulator working parameter data of the fault simulator to obtain a digital twin model, fault simulation result data, a fault performance evaluation index, a fault evaluation influence factor, a fault performance evaluation correction index, a fault prediction result data, and a comprehensive fault performance evaluation index, wherein the comprehensive fault performance evaluation index is obtained by calculating the mechanical fault evaluation influence factor, the electrical fault evaluation influence factor, and the control fault evaluation influence factor as well as the mechanical fault performance evaluation index, the electrical fault performance evaluation index, and the control fault performance evaluation index; The judgment and evaluation module is used to determine the type of fault to be focused on based on the fault performance evaluation correction index, and to make reliability judgments on industrial equipment based on the comprehensive fault performance evaluation index.

Images