CN113988188A - Fault diagnosis method, fault diagnosis device, electronic equipment and storage medium - Google Patents
Fault diagnosis method, fault diagnosis device, electronic equipment and storage medium Download PDFInfo
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- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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Abstract
The fault diagnosis method, the fault diagnosis device, the electronic equipment and the storage medium obtain system state 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 working condition event information based on the working condition information and the event information; inputting the working condition event information into a pre-established Petri network diagnosis model, and determining the probability of faults corresponding to each fault mode in the traction converter, wherein each fault mode comprises the following steps: operating condition event information; and determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
Description
Technical Field
The present disclosure relates to the field of traction converter control, and in particular, to a fault diagnosis method, a fault diagnosis apparatus, an electronic device, and a storage medium.
Background
In the running process of trains such as locomotives, motor train units and the like, any tiny or potential faults and hidden dangers can cause chain reaction to cause accidents and even cause disastrous results if the tiny or potential faults and hidden dangers cannot be diagnosed and found in time. The traction system is used as the heart of a high-speed train, and the running state of the traction system is influenced by factors such as complex running environment, corrosion, temperature, humidity, power supply surge and static electricity, so that the traction system is easy to break down and cannot be eliminated in a periodic maintenance mode. If the train has faults in the running process, the online accurate fault source positioning can be preferably realized so as to timely eliminate the faults or execute a proper isolation protection strategy. If the fault cause is not diagnosed in time and the fault is eliminated, driving accidents can be caused, the normal operation of the train is delayed, and the transportation order of the whole line and even the whole road is influenced. Therefore, research on diagnosis and prediction of the traction system fault is carried out, and the method has extremely important significance for improving the operation reliability of the high-speed train.
At present, fault diagnosis of a train traction system is still mainly based on acquisition of sensor signals, and fault detection methods such as simple over-threshold alarm and the like are adopted, such as detection and protection functions of overvoltage and overcurrent at the side of a traction system network, overcurrent input and output of a traction converter, intermediate direct current overvoltage/undervoltage, overhigh/overlow temperature and water pressure of a cooling system and the like. However, such a detection method belongs to the detection of fault characterization, and cannot diagnose the true reason of the occurrence of such characterization, generally, the vehicle needs to be stopped temporarily and be checked by a driver or system maintenance personnel, and the accurate fault positioning of the traction system cannot be realized.
Disclosure of Invention
In view of the above problems, the present application provides a fault diagnosis method, apparatus, electronic device, and storage medium.
The application provides a fault diagnosis method, which comprises the following steps:
acquiring system state information and sensor information of a traction converter;
determining operating condition information based on the system state information and determining event information based at least on the sensor information;
determining working condition event information based on the working condition information and the event information;
inputting the working condition event information into a Petri network diagnosis model, and determining the probability of the fault corresponding to each fault mode in the traction converter, wherein each fault mode comprises the following steps: operating condition event information;
and determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
In some embodiments, the method further comprises:
the method comprises the steps of obtaining system principle information, control logic information and first historical data of a traction converter;
determining a Petri Net diagnostic model based on the system principle information, the control logic information, and the first historical data.
In some embodiments, said determining a Petri net diagnostic model based on said system principles information, said control logic information, and said first historical data comprises:
analyzing fault working condition events based on the system principle information to obtain a working condition event information set;
determining a time sequence change rule of the working condition event based on the control logic information and the working condition event information set;
and establishing a Petri network diagnosis model based on the working condition event time sequence change rule and the first historical data.
In some embodiments, the building a Petri net diagnostic model based on the operating condition event time-series change rule and the first historical data includes:
determining an initial Petri network diagnosis model based on the working condition event change rule;
determining a first triggering probability of each transition node in the initial Petri net diagnostic model based on the first historical data;
determining the Petri Net diagnostic model based on the first trigger probability and the initial Petri Net diagnostic model.
In some embodiments, the determining a target fault of the traction converter based on the probability of the fault corresponding to each fault mode includes:
determining a probability maximum value from the probabilities of the faults corresponding to the fault modes;
and determining the fault corresponding to the maximum probability value as a target fault.
In some embodiments, said determining event information based at least on said sensor information comprises:
identifying the event information based on the sensor information and the set of operating condition event information;
the method further comprises the following steps:
and outputting the target fault and a fault mode corresponding to the target fault to prompt a target person to process.
In some embodiments, the method further comprises:
acquiring second historical data;
determining a second triggering probability of each transition node in the Petri network diagnostic model based on the second historical data;
updating the Petri Net diagnostic model based on the second trigger.
An embodiment of the present application provides a fault diagnosis device, including:
the first acquisition module is used for acquiring system state information and sensor information of the traction converter;
a first determination module to determine operating condition information based on the system state information and to determine event information based at least on the sensor information;
the second determining module is used for determining working condition event information based on the working condition information and the event information;
a third determining module, configured to input the operating condition event information to a Petri net diagnostic model, and determine a probability of a fault corresponding to each fault mode in the traction converter, where each fault mode includes: operating condition event information;
and the fourth determination module is used for determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, executes any one of the above fault diagnosis methods.
The present embodiment provides a storage medium storing a computer program, which can be executed by one or more processors, and can be used to implement any one of the above-described fault diagnosis methods.
According to the fault diagnosis method, the fault diagnosis device, the electronic equipment and the storage medium, the system state information and the sensor information of the traction converter are obtained, then the working condition information is determined based on the system state information, the time information is determined based on the sensor information, the working condition event information is determined based on the public information and the time information, then the working condition time information is input to the Petri network diagnosis model, the probability of faults corresponding to each fault mode in the traction converter is determined, therefore the target fault of the traction converter is determined based on the probability of the fault corresponding to each fault mode, and the fault of the traction converter can be accurately positioned.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of a fault diagnosis method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a Petri Net diagnostic model provided by an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating a method for fault diagnosis according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a fault diagnosis device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is 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.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Based on the problems in the related art, the embodiments of the present application provide a fault diagnosis method, which is applied to an electronic device, where the electronic device may be a server and a client, and the client may be a desktop computer, a tablet computer, a notebook computer, and the like. The functions implemented by the fault diagnosis method provided by the embodiment of the 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.
An embodiment of the present application provides a fault diagnosis method, and fig. 1 is a schematic flow chart illustrating an implementation process of the fault diagnosis method provided in the embodiment of the present application, and as shown in fig. 1, the fault diagnosis method includes:
and step S101, obtaining system state information and sensor information of the traction converter.
In the embodiment of the application, the electronic equipment can be connected with a sensor of the traction converter to acquire a sensor signal in the working process of the traction converter, and the electronic equipment can be connected with a processor of the traction converter and used for acquiring system state information of the traction converter in the working process from the processor of the traction converter.
Step S102, determining working condition information based on the system state information, and determining event information based on at least the sensor information.
In this application embodiment, electronic equipment can carry out operating mode discernment based on system state information to confirm operating mode information, in this application embodiment, to traction converter, there are a plurality of operating condition in inside, and its system action of different operating mode is also different with corresponding failure mode. When the traction converter system fails, due to the control and protection effects of the traction converter system, complex conversion among a plurality of working conditions often exists inside the traction converter system, and working condition information can be determined through system state information. In the embodiment of the application, all the working condition information can be determined in advance based on the system principle, and defined, and then after the system state information is determined, the working condition information can be determined based on the definition of each working condition information.
In the embodiment of the application, the event information can be determined based on the sensor information, and the event information is based on the change which can be detected by the sensor and the state information collected by the electronic equipment, such as the sensor sampling value overrun, the contactor action and the like.
And step S103, determining working condition event information based on the working condition information and the event information.
In the embodiment of the application, after a fault occurs, different working condition information corresponds to different event information sets, so that the working condition event information can be determined based on the corresponding relation between the different working condition information and the event information. Illustratively, the operating condition event information is represented by Wi:EjTo indicate.
Step S104, inputting the working condition event information into a pre-established Petri network diagnosis model, and determining the probability of faults corresponding to each fault mode in the traction converter, wherein each fault mode comprises the following steps: and (4) operating condition event information.
In the embodiment of the application, the Petri network diagnosis model is used for predicting the probability of the fault corresponding to the fault mode corresponding to the working condition time information. In the embodiment of the application, system principle information, control logic information and first historical data of the traction converter can be obtained in advance; determining a Petri Net diagnostic model based on the system principle information, the control logic information, and the first historical data, the Petri Net diagnostic model comprising: the corresponding relation of each failure mode, failure and failure probability.
And step S105, determining a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
In the embodiment of the application, after the probability of the fault corresponding to each fault mode corresponding to the public event is determined, the electronic device may select the fault with the highest probability and determine the fault as the target fault.
According to the fault diagnosis method, the system state information and the sensor information of the traction converter are obtained, then the working condition information is determined based on the system state information, the time information is determined based on the sensor information, the working condition event information is determined based on the public information and the time information, then the working condition time information is input to the Petri network diagnosis model, the probability of faults corresponding to each fault mode in the traction converter is determined, therefore, the target fault of the traction converter is determined based on the probability of the fault corresponding to each fault mode, and the fault of the traction converter can be accurately positioned.
In some embodiments, before step S101, the method further comprises:
and step S1, obtaining system principle information, control logic information and first historical data of the traction converter.
In the embodiment of the application, system principle information, control logic information and first historical data of the traction converter can be acquired through input of input equipment, wherein the input equipment can be a keyboard, a mouse, voice input equipment and the like; the data can also be acquired through the input of an external storage device, wherein the external storage device can be a U disk, a mechanical hard disk and the like; or may be obtained by means of network reception, such as the internet, a local area network; or may be obtained by reading local data, etc. The first history data includes: the probability of conversion between any two pieces of working condition event information, the probability of any one working condition and the probability of any one event.
Step S2, determining a Petri Net diagnostic model based on the system principle information, the control logic information and the first historical data.
In this embodiment of the application, step S2 may be implemented as follows:
and step S21, analyzing the fault working condition event based on the system principle information to obtain a working condition event information set.
In the embodiment of the present application, a set S of operating conditions that the system may experience is assumedw＝{Wi,i＝1,…,NwIn which N iswIs the number of conditions. Set S of possible events under different working conditions after faultE＝{Ej，j＝1,…,NEIn which N isEFor the maximum number of all possible events, the working condition set and the event set can be analyzed to obtain a working condition event information set.
Illustratively, the set of operating condition event information includes: wi1:Ej1、Wi2:Ej2、Wi3:Ej3。
And step S22, determining the time sequence change rule of the working condition event based on the control logic information and the working condition event information set.
In the embodiment of the application, the working condition event time sequence change rule can be established based on the control logic information and the working condition event information set.
Taking the above example into account, for example, when a fault Cx occurs, the possible timing changes of the operating condition event are: wi1:Ej1→Wi2:Ej2→Wi3:Ej3. That is, the timing variation of the operating condition event corresponding to the fault Cx includes: wi1:Ej1→Wi2:Ej2→Wi3:Ej3. Wherein, Wi1、Wi2、Wi3E Sw is the working condition that a certain fault Cx may experience after the occurrence, Ej1、Ej2Ej 3E SE is an event set corresponding to each experienced working condition; referred to herein as timing feature length; "→" indicates a condition transition symbol.
And step S23, establishing a Petri network diagnosis model based on the working condition event time sequence change rule and the first historical data.
In this embodiment of the application, step S23 may be implemented as follows:
and step S31, determining an initial Petri network diagnosis model based on the working condition event change rule.
The initial Petri network diagnosis model is only provided with the first triggering probability of each transition node.
Step S32, determining first triggering probabilities of the transition nodes in the initial Petri net diagnosis model based on the first historical data.
In the embodiment of the 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 may be regarded as a working condition event transition node.
Step S33, determining the Petri Net diagnostic model based on the first trigger probability and the initial Petri Net diagnostic model.
And giving corresponding first trigger probability to each transition node to obtain the Petri network diagnosis model. Fig. 2 is a schematic diagram of a Petri net diagnostic model provided in an embodiment of the present application, as shown in fig. 2,
Wi1:Ej1→Wi2:Ej2→Wi3:Ej3as a failure mode, Cx1Is Wi1:Ej1→Wi2:Ej2→Wi3:Ej3Corresponding fault, P9Is Cx1The probability of (c). p is a radical oft1，pt2And pt3Indicating the probability of the transitions t1, t2, and t3 triggering. t1, t2 and t3 are transition nodes. In practical application, after the working condition event information is determined, the working condition event information can be input into the Petri network diagnosis model, and the probability of the fault corresponding to each fault mode is determined.
In some embodiments, the step S105 "determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode" may be implemented by:
in step S51, a probability maximum value is determined from the probabilities of the failures corresponding to the failure modes.
In step S52, the fault corresponding to the probability maximum is determined as the target fault.
In some embodiments, after step S105, the method further comprises:
and S106, outputting the target fault and the corresponding fault mode to prompt a target person to process.
In some embodiments, after step S33, the method further comprises:
in step S34, second history data is acquired.
In the embodiment of the present application, when performing fault location diagnosis, as the generated data increases, second history data may be acquired again, where the second history data includes: the probability of conversion between any two pieces of working condition event information, the probability of any one working condition and the probability of any one event.
Step S35, determining second triggering probabilities of all the transition nodes in the Petri network diagnosis model based on the second historical data;
step S36, updating the Petri Net diagnostic model based on the second trigger.
In the embodiment of the application, the Petri network diagnosis model can be continuously improved through the second historical data, the probability distribution of each fault is updated, and the accuracy of fault diagnosis is improved.
Based on the foregoing embodiments, a fault diagnosis method is further provided in an embodiment of the present application, and fig. 3 is a schematic diagram of a principle of the fault diagnosis method provided in the embodiment of the present application, as shown in fig. 3, the fault diagnosis method includes: the off-line design and the on-line implementation are two stages. The algorithm is designed in an off-line mode, namely a Petri network diagnosis model is constructed, and the on-line implementation stage is an application stage.
In the off-line design stage, based on a system principle (same as the system principle information in the above embodiment) and historical data (same as the first historical data in the above embodiment), and in combination with a train traction system related control logic (same as the control logic information in the above embodiment), a working condition event rule related to each fault is analyzed, and a Petri network diagnosis model of the working condition event of each fault is established. Specifically, fault working condition event analysis is carried out through a system principle, a working condition event set is determined, a working condition event time sequence change rule is determined based on control logic and the working condition event set, and then a Petri network diagnosis model is determined based on historical data and the working condition event time sequence change rule.
In the online implementation stage, sensor signals are collected in real time, and whether each event is established or not is calculated by combining the definition of each event in a working condition event set; and identifying the working condition based on the system state, calculating the Petri network model based on the working condition event information, performing diagnosis decision based on the model calculation result, and outputting the fault type with the maximum probability (the same as the target fault in the embodiment) as the diagnosis result.
For a traction converter, a plurality of operation conditions often exist in the traction converter, and the system behaviors and corresponding fault modes of different operation conditions are different. When the system fails, complex conversion among a plurality of working conditions often exists inside the system due to the control and protection functions of the system. Therefore, thisAnd performing probability Petri network modeling based on the working condition event sets related to various faults and the time sequence change rules thereof for subsequent real-time fault diagnosis. Assume a set S of operating conditions that the system may experiencew＝{Wi,i＝1,…,NwIn which N iswIs the number of conditions. Set S of possible events under different working conditions after faultE＝{Ej，j＝1,…,NEIn which N isEThe maximum number of all possible events. Here, the event refers to a change that can be detected based on the sensor and status information collected by the system, such as an overrun of sensor sampling values, a contactor action, and the like. Suppose a certain type of failure CxThere is a possible failure mode of W when it occursi1:Ej1→Wi2:Ej2→Wi3:Ej3Wherein W isi1…,Wi3∈SwFor a certain fault CxOperating conditions which may be experienced after the occurrence, Ej1…,Ej3∈SEEvent sets corresponding to the experienced working conditions; referred to herein as timing feature length; "→" indicates a condition transition symbol. In the embodiment of the application, each probability is distributed according to historical data, and after the diagnosis, the fault C is obtainedxProbability of corresponding failure mode 1, failure CxAnd other fault modes are also used for acquiring the probability of the fault mode by using the method, and finally the fault mode with the highest probability is taken as a fault decision result to prompt a driver or a maintenance worker to process.
The embodiment of the application provides a fault diagnosis method based on a Petri network model of a working condition event, which comprises the steps of establishing a Petri network diagnosis model based on the operating condition of a traction converter by utilizing historical mass data according to system principle parameters in an off-line state, carrying out probability distribution on each fault, leading the Petri network diagnosis model into an on-line diagnosis program, obtaining current working condition information in real time, obtaining the probability distribution of the faults, and taking a fault point with the maximum probability as a final diagnosis result.
Based on the foregoing embodiments, the embodiments of the present application provide a fault diagnosis apparatus, where each module included in the apparatus and each unit included in each module may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a microprocessor unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
Fig. 4 is a schematic structural diagram of a fault diagnosis device provided in an embodiment of the present application, and as shown in fig. 4, a fault diagnosis device 400 includes:
a first obtaining module 401, configured to obtain system state information and sensor information of the traction converter;
a first determination module 402 configured to determine operating condition information based on the system state information and determine event information based on at least the sensor information;
a second determining module 403, configured to determine operating condition event information based on the operating condition information and the event information;
a third determining module 404, configured to input the operating condition event information to a Petri net diagnostic model, and determine a probability of a fault corresponding to each fault mode in the traction converter, where each fault mode includes: operating condition event information;
a fourth determining module 405, configured to determine a target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
In some embodiments, the fault diagnosis apparatus 400 includes:
the second acquisition module is used for acquiring system principle information, control logic information and first historical data of the traction converter;
a fifth determination module to determine a Petri Net diagnostic model based on the system principle information, the control logic information, and the first historical data.
In some embodiments, the fifth determining module comprises:
the first determining unit is used for analyzing fault working condition events based on the system principle information to obtain a working condition event information set;
the second determining unit is used for determining a working condition event time sequence change rule based on the control logic information and the working condition event information set;
and the establishing unit is used for establishing a Petri network diagnosis model based on the working condition event time sequence change rule and the first historical data.
In some embodiments, the establishing unit comprises:
the first determining subunit is used for determining an initial Petri network diagnosis model based on the working condition event change rule;
the second determining subunit is used for determining a first triggering probability of each transition node in the initial Petri network diagnostic model based on the first historical data;
a third determining subunit, configured to determine the Petri net diagnostic model based on the first trigger probability and the initial Petri net diagnostic model.
In some embodiments, the fourth determining module 405 includes:
a third determining unit, configured to determine a maximum probability value from the probabilities of the faults corresponding to the respective fault modes;
a third determining unit, configured to determine a fault corresponding to the maximum probability value as a target fault;
in some embodiments, the fault diagnosis apparatus 400 includes:
the output module is used for outputting the target fault and the corresponding fault mode so as to prompt a target person to process;
in some embodiments, the first determining module 402 includes:
a fourth determination unit configured to identify the event information based on the sensor information and the set of operating condition event information;
in some embodiments, the fault diagnosis apparatus 400 includes:
the third acquisition module is used for acquiring second historical data;
a sixth determining module, configured to determine, based on the second historical data, second trigger probabilities of the transition nodes in the Petri net diagnostic model;
an update module to update the Petri Net diagnostic model based on the second trigger.
It should be noted that, in the embodiment of the present application, if the method for determining development parameters is implemented in the form of a software functional module and is sold or used as a standalone product, the method may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the steps in the fault diagnosis method provided in the above embodiment.
The embodiment of the application provides an electronic device; fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 5, 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. Wherein the communication bus 502 is configured to enable connective 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, among others. The processor 501 is configured to execute a program of the fault diagnosis method stored in the memory to implement the steps in the fault diagnosis method provided in the above-described embodiments.
The above description of the electronic device and storage medium embodiments, similar to the description of the method embodiments above, has similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A fault diagnosis method, comprising:
acquiring system state information and sensor information of a traction converter;
determining operating condition information based on the system state information and determining event information based at least on the sensor information;
determining working condition event information based on the working condition information and the event information;
inputting the working condition event information into a pre-established Petri network diagnosis model, and determining the probability of faults corresponding to each fault mode in the traction converter, wherein each fault mode comprises the following steps: operating condition event information;
and determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
2. The method of claim 1, further comprising:
the method comprises the steps of obtaining system principle information, control logic information and first historical data of a traction converter;
determining a Petri Net diagnostic model based on the system principle information, the control logic information, and the first historical data.
3. The method of claim 2, wherein determining a Petri Net diagnostic model based on the system principles information, the control logic information, and the first historical data comprises:
analyzing fault working condition events based on the system principle information to obtain a working condition event information set;
determining a time sequence change rule of the working condition event based on the control logic information and the working condition event information set;
and establishing a Petri network diagnosis model based on the working condition event time sequence change rule and the first historical data.
4. The method of claim 3, wherein the building a Petri Net diagnostic model based on the operating condition event timing variation rule and the first historical data comprises:
determining an initial Petri network diagnosis model based on the working condition event change rule;
determining a first triggering probability of each transition node in the initial Petri net diagnostic model based on the first historical data;
determining the Petri Net diagnostic model based on the first trigger probability and the initial Petri Net diagnostic model.
5. The method of claim 1, wherein determining the target fault for the traction converter based on the probability of the fault corresponding to each fault mode comprises:
determining a probability maximum value from the probabilities of the faults corresponding to the fault modes;
determining the fault corresponding to the maximum probability value as a target fault;
the method further comprises the following steps:
and outputting the target fault and the corresponding fault mode to prompt a target person to process.
6. The method of claim 3, wherein the determining event information based at least on the sensor information comprises:
identifying the event information based on the sensor information and the set of operating condition event information.
7. The method of claim 4, further comprising:
acquiring second historical data;
determining a second triggering probability of each transition node in the Petri network diagnostic model based on the second historical data;
updating the Petri Net diagnostic model based on the second trigger.
8. A failure diagnosis device characterized by comprising:
the first acquisition module is used for acquiring system state information and sensor information of the traction converter;
a first determination module to determine operating condition information based on the system state information and to determine event information based at least on the sensor information;
the second determining module is used for determining working condition event information based on the working condition information and the event information;
a third determining module, configured to input the operating condition event information to a Petri net diagnostic model, and determine a probability of a fault corresponding to each fault mode in the traction converter, where each fault mode includes: operating condition event information;
and the fourth determination module is used for determining the target fault of the traction converter based on the probability of the fault corresponding to each fault mode.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, performs the fault diagnosis method according to any one of claims 1 to 7.
10. A storage medium storing a computer program executable by one or more processors and operable to implement a fault diagnosis method as claimed in any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115951263A (en) * | 2023-03-13 | 2023-04-11 | 广东工业大学 | Method for diagnosing grounding fault of main loop of traction system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115951263A (en) * | 2023-03-13 | 2023-04-11 | 广东工业大学 | Method for diagnosing grounding fault of main loop of traction system |
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