Fault diagnosis method, device, electronic device and storage medium

Abstract

The present application provides a fault diagnosis method, device, electronic device and storage medium, which obtain system status information and sensor information of a traction inverter; determine operating condition information based on the system status information, and determine event information based at least on the sensor information; determine operating condition event information based on the operating condition information and the event information; input the operating condition event information into a pre-established Petri net diagnostic model to determine the probability of faults corresponding to each fault mode in the traction inverter, wherein each fault mode includes: operating condition event information; determine the target fault of the traction inverter based on the probability of faults corresponding to each fault mode.

Description

Technical Field

[0001] The present application relates to the field of traction converter control, and in particular to a fault diagnosis method, device, electronic device and storage medium. Background Art

[0002] During the operation of locomotives, EMUs and other trains, any minor or potential faults and hidden dangers may trigger a chain reaction and cause accidents, or even lead to catastrophic consequences if they are not diagnosed and discovered in time. As the "heart" of high-speed trains, the traction system is prone to failures due to its complex operating environment. Factors such as corrosion, temperature, humidity, power surges, and static electricity will affect its operating status, and it is very easy to fail, and it cannot be eliminated through regular maintenance. If a train fails during operation, it is best to accurately locate the fault source online so that the fault can be eliminated in time or appropriate isolation and protection strategies can be implemented. If the cause of the fault is not diagnosed and eliminated in time, it may cause traffic accidents, delay the normal operation of the train, and affect the transportation order of the entire line and even the entire road. Therefore, conducting research on traction system fault diagnosis and prediction is of great significance to improving the operating reliability of high-speed trains.

[0003] At present, the fault diagnosis of train traction system is still mainly based on the acquisition of sensor signals, using simple over-threshold alarm and other fault detection methods, such as overvoltage and overcurrent on the grid side of the traction system, overcurrent on the input and output of the traction converter, overvoltage / undervoltage of the intermediate DC, and over-high / under-low temperature and water pressure of the cooling system. However, such detection methods belong to the detection of fault manifestations, and cannot diagnose the real cause of such manifestations. Generally, temporary parking is required for the driver or system maintenance personnel to conduct investigation, and accurate fault location of the traction system cannot be achieved. Summary of the invention

[0004] In response to the above problems, the present application provides a fault diagnosis method, device, electronic device and storage medium.

[0005] The present application provides a fault diagnosis method, comprising:

[0006] Obtaining system status information and sensor information of the traction converter;

[0007] determining operating condition information based on the system state information, and determining event information based at least on the sensor information;

[0008] Determining operating condition event information based on the operating condition information and the event information;

[0009] Inputting the operating condition event information into a Petri net diagnostic model to determine the probability of a fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information;

[0010] A target fault of the traction converter is determined based on the probability of the fault corresponding to each fault mode.

[0011] In some embodiments, the method further comprises:

[0012] Acquiring system principle information, control logic information and first historical data of the traction converter;

[0013] A Petri net diagnosis model is determined based on the system principle information, the control logic information and the first historical data.

[0014] In some embodiments, determining a Petri net diagnostic model based on the system principle information, the control logic information and the first historical data includes:

[0015] Performing fault condition event analysis based on the system principle information to obtain a condition event information set;

[0016] Determine a temporal change rule of an operating condition event based on the control logic information and the operating condition event information set;

[0017] A Petri net diagnosis model is established based on the temporal variation law of the operating condition events and the first historical data.

[0018] In some embodiments, the establishing of a Petri net diagnosis model based on the temporal variation law of the operating condition event and the first historical data includes:

[0019] Determine an initial Petri net diagnosis model based on the change rules of the working condition events;

[0020] Determine a first trigger probability of each transition node in the initial Petri net diagnosis model based on the first historical data;

[0021] The Petri net diagnostic model is determined based on the first trigger probability and the initial Petri net diagnostic model.

[0022] In some embodiments, determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode includes:

[0023] Determine the maximum probability from the probabilities of failures corresponding to the various failure modes;

[0024] The fault corresponding to the maximum probability is determined as the target fault.

[0025] In some embodiments, the determining event information based at least on the sensor information comprises:

[0026] Based on the sensor information and the operating condition event information set, identifying the event information;

[0027] The method further comprises:

[0028] The target fault and the fault mode corresponding to the target fault are output to prompt the target personnel to handle it.

[0029] In some embodiments, the method further comprises:

[0030] Obtaining second historical data;

[0031] Determining a second trigger probability of each transition node in the Petri net diagnosis model based on the second historical data;

[0032] The Petri net diagnosis model is updated based on the second trigger.

[0033] The present application provides a fault diagnosis device, including:

[0034] A first acquisition module, used to acquire system status information and sensor information of the traction converter;

[0035] a first determination module, configured to determine operating condition information based on the system state information, and determine event information based at least on the sensor information;

[0036] A second determining module, configured to determine operating condition event information based on the operating condition information and the event information;

[0037] a third determination module, configured to input the operating condition event information into a Petri net diagnosis model to determine the probability of a fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information;

[0038] The fourth determination module is configured to determine a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.

[0039] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned fault diagnosis methods is executed.

[0040] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned fault diagnosis methods.

[0041] The present application provides a fault diagnosis method, device, electronic device and storage medium, which obtains system status information and sensor information of a traction inverter, then determines operating condition information based on the system status information, determines time information based on sensor information, determines operating condition event information based on public information and time information, and then inputs the operating condition time information into a Petri net diagnosis model to determine the probability of faults corresponding to each fault mode in the traction inverter, thereby determining the target fault of the traction inverter based on the probability of faults corresponding to each fault mode, and can achieve accurate positioning of the traction inverter fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings.

[0043] Figure 1 A schematic diagram of an implementation flow of a fault diagnosis method provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of a Petri net diagnostic model provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of the principle of a fault diagnosis method provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of the structure of a fault diagnosis device provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0048] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0050] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0051] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0053] Based on the problems existing in the related art, an embodiment of the present application provides a fault diagnosis method, which is applied to an electronic device, which may be a server and a client, and which may be a desktop computer, a tablet computer, a laptop computer, etc. The function implemented by the fault diagnosis method provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic device, wherein the program code can be stored in a computer storage medium.

[0054] The present application provides a fault diagnosis method. Figure 1 A schematic diagram of the implementation flow of a fault diagnosis method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0055] Step S101, obtaining system status information and sensor information of a traction converter.

[0056] In an embodiment of the present application, the electronic device can be connected to the sensor of the traction inverter to collect sensor signals during the operation of the traction inverter. The electronic device can be connected to the processor of the traction inverter to obtain system status information of the traction inverter when it is operating.

[0057] Step S102: determining operating condition information based on the system status information, and determining event information based at least on the sensor information.

[0058] In an embodiment of the present application, the electronic device can identify the working condition based on the system status information to determine the working condition information. In an embodiment of the present application, for the traction inverter, there are multiple operating conditions inside, and the system behavior and corresponding failure mode of different working conditions are also different. When a traction inverter system fails, due to the control and protection function of the traction inverter system, there are often complex conversions between multiple working conditions inside, and the working condition information can be determined through the system status information. In an embodiment of the present application, all the working condition information can be determined in advance based on the system principle, and all the working condition information can be defined. Then, after determining the system status information, the working condition information can be determined based on the definition of each working condition information.

[0059] In the embodiment of the present application, event information can be determined based on sensor information, and the event information is based on changes that can be detected by sensors and status information collected by the electronic device, such as sensor sampling value exceeding the limit, contactor action, etc.

[0060] Step S103: determining operating condition event information based on the operating condition information and the event information.

[0061] In the embodiment of the present application, after a fault occurs, different operating condition information corresponds to different event information sets. Therefore, the operating condition event information can be determined based on the correspondence between different operating condition information and event information. i :E j To express.

[0062] Step S104, inputting the operating condition event information into a pre-established Petri net diagnosis model to determine the probability of a fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information.

[0063] In the embodiment of the present application, the Petri net diagnosis model is used to predict the probability of the fault corresponding to the fault mode corresponding to the working condition time information. In the embodiment of the present application, the system principle information, control logic information and first historical data of the traction converter can be obtained in advance; based on the system principle information, the control logic information and the first historical data, the Petri net diagnosis model is determined, and the Petri net diagnosis model includes: the corresponding relationship between each fault mode, fault and fault probability.

[0064] Step S105 : determining a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.

[0065] In the embodiment of the present application, after determining the probability of failures corresponding to various failure modes corresponding to the public event, the electronic device can select the failure with the highest probability and determine it as the target failure.

[0066] The present application provides a fault diagnosis method, which obtains system status information and sensor information of a traction inverter, determines operating condition information based on the system status information, determines time information based on sensor information, determines operating condition event information based on public information and time information, and then inputs the operating condition time information into a Petri net diagnosis model to determine the probability of faults corresponding to each fault mode in the traction inverter, thereby determining the target fault of the traction inverter based on the probability of faults corresponding to each fault mode, and can achieve accurate positioning of the traction inverter fault.

[0067] In some embodiments, before step S101, the method further includes:

[0068] Step S1, obtaining system principle information, control logic information and first historical data of a traction converter.

[0069] In the embodiment of the present application, the system principle information, control logic information and first historical data of the traction converter can be obtained through input from an input device, which can be a keyboard, a mouse, a voice input device, etc.; it can also be obtained through input from an external storage device, which can be a USB flash drive, a mechanical hard disk, etc.; it can also be obtained through network reception, such as the Internet, a local area network; it can also be obtained by reading local data, etc. The first historical data includes: the probability of conversion between any two working condition event information, the probability of any working condition, and the probability of any event.

[0070] Step S2, determining a Petri net diagnosis model based on the system principle information, the control logic information and the first historical data.

[0071] In the embodiment of the present application, step S2 can be implemented in the following manner:

[0072] Step S21, performing fault condition event analysis based on the system principle information to obtain a condition event information set.

[0073] In the embodiment of the present application, it is assumed that the system may experience a set of operating conditions S w = {W i ,i=1,…,N w}, where N w is the number of working conditions. The possible event set S under different working conditions after a fault occurs E ={E j ,j=1,…,N E}, where N E is the maximum number of all possible events. The working condition set and event set can be analyzed to obtain the working condition event information set.

[0074] Exemplarily, the operating condition event information set includes: i1 :E j1 , W i2 :E j2 , W i3 :E j3 .

[0075] Step S22: determining a temporal variation rule of an operating condition event based on the control logic information and the operating condition event information set.

[0076] In an embodiment of the present application, a temporal variation rule of operating condition events can be established based on control logic information and an operating condition event information set.

[0077] Continuing with the above example, when a fault Cx occurs, the possible operating condition event sequence changes are: i1 :E j1 →W i2 :E j2 →W i3 :E j3 That is, the timing changes of the operating conditions corresponding to the fault Cx include: i1 :E j1 →W i2 :E j2 →W i3 :E j3 Among them, W i1 , W i2 , W i3 ∈Sw is the possible working condition after a certain fault Cx occurs, E j1 、E j2 , Ej3∈SE is the set of events corresponding to each operating condition experienced; it is called the time series characteristic length here; “→” is the operating condition conversion symbol.

[0078] Step S23, establishing a Petri net diagnosis model based on the temporal variation law of the operating condition events and the first historical data.

[0079] In the embodiment of the present application, step S23 can be implemented in the following manner:

[0080] Step S31, determining an initial Petri net diagnosis model based on the change rule of the operating condition event.

[0081] In the initial Petri net diagnosis model, there is only no first trigger probability of each transition node.

[0082] Step S32: determining a first trigger probability of each transition node in the initial Petri net diagnosis model based on the first historical data.

[0083] In the embodiment of the present application, the first trigger probability of each transition node can be determined based on the first historical data. In the embodiment of the present application, each transition node can be considered as a working condition event conversion node.

[0084] Step S33: determining the Petri net diagnosis model based on the first trigger probability and the initial Petri net diagnosis model.

[0085] Each transition node may be assigned a corresponding first trigger probability, thereby obtaining the Petri net diagnosis model. Figure 2 A schematic diagram of a Petri net diagnostic model provided in an embodiment of the present application is shown in FIG. Figure 2 As shown,

[0086] W i1 :E j1 →W i2 :E j2 →W i3 :E j3 is the failure mode, C x1 W i1 :E j1 →W i2 :E j2 →W i3 :E j3 The corresponding fault, P9 is C x1 The probability of t1 , p t2 and p t3 Indicates the probability of transitions t1, t2 and t3 being triggered. t1, t2 and t3 are transition nodes. In practical applications, once the operating event information is determined, it can be input into the Petri net diagnosis model to determine the probability of failure corresponding to each failure mode.

[0087] In some embodiments, step S105 of "determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode" can be implemented in the following manner:

[0088] Step S51, determining the maximum probability from the probabilities of failures corresponding to various failure modes.

[0089] Step S52: determine the fault corresponding to the maximum probability as the target fault.

[0090] In some embodiments, after step S105, the method further includes:

[0091] Step S106, outputting the target fault and the corresponding fault mode to prompt the target personnel to handle it.

[0092] In some embodiments, after step S33, the method further includes:

[0093] Step S34, obtaining second historical data.

[0094] In an embodiment of the present application, when performing fault location diagnosis, as the generated data increases, second historical data can be obtained again, and the second historical data includes: the probability of conversion between any two operating condition event information, the probability of any operating condition, and the probability of any event.

[0095] Step S35, determining a second trigger probability of each transition node in the Petri net diagnosis model based on the second historical data;

[0096] Step S36: updating the Petri net diagnosis model based on the second trigger.

[0097] In the embodiment of the present application, the Petri net diagnosis model can be continuously improved through the second historical data, the probability distribution of each fault can be updated, and the accuracy of fault diagnosis can be improved.

[0098] Based on the above embodiments, the present application further provides a fault diagnosis method. Figure 3 A schematic diagram of the principle of a fault diagnosis method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the fault diagnosis method includes two stages: offline design and online implementation. The algorithm is divided into offline design, which is to build a Petri net diagnosis model, and online implementation, which is the application stage.

[0099] In the offline design phase, based on the system principle (the same as the system principle information in the above embodiment) and historical data (the same as the first historical data in the above embodiment), and in combination with the relevant control logic of the train traction system (the same as the control logic information in the above embodiment), the operating condition event rules related to each fault are analyzed, and a Petri net diagnosis model of the operating condition events of each fault is established. Specifically, the fault operating condition event analysis is performed based on the system principle to determine the operating condition event set, the operating condition event time sequence change rule is determined based on the control logic and the operating condition event set, and then the Petri net diagnosis model is determined based on the historical data and the operating condition event time sequence change rule.

[0100] In the online implementation stage, sensor signals are collected in real time, and the definitions of each event in the working condition event set are combined to calculate whether each event is established; the working condition is identified based on the system state, and then the Petri net model is calculated based on the working condition event information. Then, a diagnostic decision is made based on the model calculation results, and the fault type with the highest probability (the same as the target fault in the above embodiment) is output as the diagnostic result.

[0101] For traction converters, there are often multiple operating conditions inside, and the system behaviors and corresponding failure modes of different operating conditions are also different. When a system fails, due to the control and protection of the system, there are often complex transitions between multiple operating conditions inside. Therefore, this application uses probabilistic Petri net modeling based on various types of fault-related operating condition event sets and their time sequence change rules for subsequent real-time fault diagnosis. Assume that the system may experience a set of operating conditions S w = {W i ,i=1,…,N w}, where N w is the number of working conditions. The possible event set S under different working conditions after a fault occurs E ={E j ,j=1,…,N E}, where N E is the maximum number of all possible events. Here, events refer to changes that can be detected based on the sensor and status information collected by the system, such as sensor sampling value exceeding the limit, contactor action, etc. Assume that a certain type of fault C x There is a possible failure mode W when it occurs i1 :E j1 →W i2 :E j2 →W i3 :E j3 , where W i1 …,W i3 ∈S w For a certain fault C x The working conditions that may be experienced after the occurrence of E j1 …,E j3 ∈S E is a set of events corresponding to each working condition experienced; it is referred to as the time series feature length; “→” is the working condition conversion symbol. In the embodiment of the present application, each probability is allocated according to historical data, and after the above diagnosis, the fault C is obtained. x The corresponding probability of failure mode 1, failure C x The above method is also used to obtain the probability of other failure modes, and finally the failure mode with the largest probability is taken as the failure decision result to prompt the driver or maintenance personnel to handle it.

[0102] The embodiment of the present application is a fault diagnosis method based on a Petri net model of operating condition events. In an offline state, according to system principle parameters, a Petri net diagnosis model based on the operating condition of the traction converter is established using historical massive data, a probability distribution is made for each fault, the Petri net diagnosis model is imported into an online diagnosis program, and the current operating condition information is obtained in real time, the probability distribution of the fault is obtained, and the fault point with the highest probability is taken as the final diagnosis result.

[0103] Based on the foregoing embodiments, the embodiments of the present application provide a fault diagnosis device, and the modules included in the device, as well as the units included in the modules, can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0104] The present application provides a fault diagnosis device. Figure 4 A schematic diagram of a fault diagnosis device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the fault diagnosis device 400 includes:

[0105] A first acquisition module 401 is used to acquire system status information and sensor information of the traction converter;

[0106] A first determination module 402, configured to determine operating condition information based on the system state information, and determine event information based at least on the sensor information;

[0107] A second determining module 403, configured to determine operating condition event information based on the operating condition information and the event information;

[0108] The third determination module 404 is used to input the operating condition event information into the Petri net diagnosis model to determine the probability of the fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information;

[0109] The fourth determination module 405 is configured to determine a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.

[0110] In some embodiments, the fault diagnosis device 400 includes:

[0111] A second acquisition module, used to acquire system principle information, control logic information and first historical data of the traction converter;

[0112] A fifth determination module is used to determine a Petri net diagnosis model based on the system principle information, the control logic information and the first historical data.

[0113] In some embodiments, the fifth determining module includes:

[0114] A first determining unit, configured to perform fault condition event analysis based on the system principle information to obtain a condition event information set;

[0115] A second determining unit, configured to determine a temporal variation rule of an operating condition event based on the control logic information and the operating condition event information set;

[0116] An establishing unit is used to establish a Petri net diagnosis model based on the temporal variation law of the working condition events and the first historical data.

[0117] In some embodiments, the establishing unit includes:

[0118] A first determining subunit, used to determine an initial Petri net diagnosis model based on the change law of the working condition event;

[0119] A second determining subunit, configured to determine a first triggering probability of each transition node in the initial Petri net diagnostic model based on the first historical data;

[0120] The third determining subunit is configured to determine the Petri net diagnostic model based on the first trigger probability and the initial Petri net diagnostic model.

[0121] In some embodiments, the fourth determining module 405 includes:

[0122] A third determining unit is used to determine the maximum probability from the probabilities of failures corresponding to various failure modes;

[0123] A third determination unit, configured to determine the fault corresponding to the maximum probability value as a target fault;

[0124] In some embodiments, the fault diagnosis device 400 includes:

[0125] An output module, used to output the target fault and the corresponding fault mode to prompt the target personnel to handle it;

[0126] In some embodiments, the first determining module 402 includes:

[0127] a fourth determining unit, configured to identify the event information based on the sensor information and the operating condition event information set;

[0128] In some embodiments, the fault diagnosis device 400 includes:

[0129] A third acquisition module is used to acquire second historical data;

[0130] a sixth determination module, configured to determine a second trigger probability of each transition node in the Petri net diagnosis model based on the second historical data;

[0131] An updating module is used to update the Petri net diagnosis model based on the second trigger.

[0132] It should be noted that in the embodiment of the present application, if the above-mentioned method for determining the development parameters 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 computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, ReadOnlyMemory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0133] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the fault diagnosis method provided in the above embodiment are implemented.

[0134] An embodiment of the present application provides an electronic device; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, and a memory 505. The communication bus 502 is configured to realize the connection and communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include a standard wired interface and a wireless interface. The processor 501 is configured to execute the program of the fault diagnosis method stored in the memory to implement the steps in the fault diagnosis method provided in the above embodiment.

[0135] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0136] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0137] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0138] 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.

[0139] 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.

[0140] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate 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.

[0141] A person of ordinary skill 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 computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, and other media that can store program codes.

[0142] Alternatively, if the above-mentioned integrated unit of the present application 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 computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0143] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fault diagnosis method, characterized in that: include: Obtaining system status information and sensor information of the traction converter; determining operating condition information based on the system state information, and determining event information based at least on the sensor information; Determining operating condition event information based on the operating condition information and the event information; Inputting the operating condition event information into a pre-established Petri net diagnostic model to determine the probability of a fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information; determining a target fault of the traction converter based on the probability of the fault corresponding to each fault mode; The method further comprises: Acquiring system principle information, control logic information and first historical data of the traction converter; Determine a Petri net diagnosis model based on the system principle information, the control logic information and the first historical data; The determining of the Petri net diagnosis model based on the system principle information, the control logic information and the first historical data includes: Performing fault condition event analysis based on the system principle information to obtain a condition event information set; Determine a temporal change rule of an operating condition event based on the control logic information and the operating condition event information set; Establishing a Petri net diagnosis model based on the temporal variation law of the working condition events and the first historical data; The establishing of a Petri net diagnosis model based on the temporal variation law of the working condition event and the first historical data comprises: Determine an initial Petri net diagnosis model based on the change rules of the working condition events; Determine a first trigger probability of each transition node in the initial Petri net diagnosis model based on the first historical data; The Petri net diagnostic model is determined based on the first trigger probability and the initial Petri net diagnostic model.

2. The method according to claim 1, characterized in that The determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode includes: Determine the maximum probability from the probabilities of failures corresponding to the various failure modes; The fault corresponding to the maximum probability is determined as the target fault; The method further comprises: The target fault and the corresponding fault mode are output to prompt the target personnel to handle it.

3. The method according to claim 1, characterized in that: The determining of event information based at least on the sensor information comprises: Based on the sensor information and the operating condition event information set, the event information is identified.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining second historical data; Determining a second trigger probability of each transition node in the Petri net diagnosis model based on the second historical data; The Petri net diagnosis model is updated based on the second trigger.

5. A fault diagnosis device for implementing the fault diagnosis method according to any one of claims 1 to 4, characterized in that: include: A first acquisition module, used to acquire system status information and sensor information of the traction converter; a first determination module, configured to determine operating condition information based on the system state information, and determine event information based at least on the sensor information; A second determining module, configured to determine operating condition event information based on the operating condition information and the event information; a third determination module, configured to input the operating condition event information into a Petri net diagnosis model to determine the probability of a fault corresponding to each fault mode in the traction converter, wherein each fault mode includes: operating condition event information; The fourth determination module is configured to determine a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fault diagnosis method according to any one of claims 1 to 4 is executed.

7. A storage medium, characterized in that: The storage medium stores a computer program, which can be executed by one or more processors and can be used to implement the fault diagnosis method as described in any one of claims 1 to 4.

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