CN115879033A - Fault diagnosis method and device - Google Patents
Fault diagnosis method and device Download PDFInfo
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- CN115879033A CN115879033A CN202211458526.5A CN202211458526A CN115879033A CN 115879033 A CN115879033 A CN 115879033A CN 202211458526 A CN202211458526 A CN 202211458526A CN 115879033 A CN115879033 A CN 115879033A
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Abstract
The invention provides a fault diagnosis method and a fault diagnosis device, wherein the fault diagnosis method comprises the following steps: and acquiring maintenance data of the ZC, and obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprises alarm information. The method of the invention classifies the fault types contained in the ZC maintenance data by using the fault diagnosis model obtained by the online learning algorithm, realizes the prediction of unknown fault types, improves the fault diagnosis efficiency and saves the time cost and the labor cost.
Description
Technical Field
The invention relates to the technical field of rail transit equipment fault detection, in particular to a fault diagnosis method and device.
Background
In the rail transit train operation control system, the intelligent operation and maintenance system can realize full-coverage acquisition on key equipment data of a signal system, and comprises a plurality of subsystems and state and alarm information of key equipment.
In the related art, an intelligent operation and maintenance system generally monitors an operating state of a Zone Controller (ZC) and identifies or predicts fault information included in ZC maintenance data in a manual detection manner, but as the ZC maintenance data to be identified increases and the fault type tends to become complicated, efficiency of manually diagnosing the fault type is low and accuracy of a diagnosis result is low.
Disclosure of Invention
The invention provides a fault diagnosis method and a fault diagnosis device, which are used for solving the defects that the efficiency is low and the diagnosis result is inaccurate when a large amount of fault types of ZC maintenance data are identified by using a manual detection mode in the prior art, and the identification efficiency of fault information in the ZC maintenance data is improved.
The invention provides a fault diagnosis method, which is applied to an intelligent operation and maintenance system and comprises the following steps:
acquiring maintenance data of a zone controller ZC;
and under the condition that the maintenance data comprise alarm information, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on sample fault data and an online learning algorithm.
According to a fault diagnosis method provided by the present invention, the maintenance data includes running state information of the ZC, train movement authorization information, fault alarm information of the ZC, and board card running state information of the ZC, and after the maintenance data transmitted by the ZC is received, the method further includes:
retrieving in a preset knowledge base by taking at least one of the running state information of the ZC, the train moving authorization information, the fault alarm information of the ZC and the board card running state information of the ZC as a retrieval condition to obtain a diagnosis result corresponding to the maintenance data;
wherein the knowledge base is determined based on the existing fault data and a diagnostic scheme corresponding to the existing fault data.
According to the fault diagnosis method provided by the invention, the online learning algorithm is a sensor algorithm, and the fault diagnosis model is obtained through the following steps:
acquiring sample fault data of the ZC, wherein the sample fault data comprises various fault classification information;
obtaining the fault characteristics based on the multiple fault classification information;
inputting the fault characteristics into an online learning model, and training the online learning model through the perceptron algorithm to obtain the fault diagnosis model.
According to the fault diagnosis method provided by the invention, the multiple fault classification information includes fault equipment, fault names, fault levels, fault contents, fault occurrence time, fault occurrence times and fault occurrence intervals, and the fault characteristics are obtained based on the multiple fault classification information, and the fault diagnosis method includes the following steps:
and respectively converting the various fault classification information into target character strings, and combining the target character strings according to a preset arrangement sequence to obtain a feature vector corresponding to the fault feature.
According to a fault diagnosis method provided by the present invention, after the obtaining of the diagnosis result corresponding to the fault type, the method further includes:
and displaying the diagnosis result on a display screen of the intelligent operation and maintenance system.
The present invention also provides a fault diagnosis apparatus including:
an acquisition module for acquiring maintenance data of a zone controller ZC;
the diagnosis module is used for obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprises alarm information, the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on sample fault data and an online learning algorithm.
According to a fault diagnosis apparatus provided by the present invention, the apparatus further includes:
and the display module is used for displaying the diagnosis result after the diagnosis result corresponding to the fault type is obtained.
The present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above fault diagnosis methods when executing the program.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a fault diagnosis method as described in any one of the above.
The present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements a fault diagnosis method as described in any one of the above.
According to the fault diagnosis method and device provided by the invention, the fault types contained in the ZC maintenance data are classified by using the fault diagnosis model obtained by the online learning algorithm, so that the unknown fault types are predicted, the fault diagnosis efficiency is improved, and the time cost and the labor cost are saved.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a fault diagnosis method provided by the present invention;
fig. 2 is one of the schematic structural diagrams of the failure diagnosis apparatus provided by the present invention;
FIG. 3 is a second schematic structural diagram of a fault diagnosis device provided in the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The fault diagnosis method and apparatus of the present invention will be described below with reference to fig. 1 to 3.
Fig. 1 is a schematic flow chart of a fault diagnosis method provided by the present invention, and as shown in fig. 1, the fault diagnosis method includes the following steps:
In this step, the intelligent operation and maintenance system may periodically receive maintenance data sent by the ZC, where the maintenance data may be running state information of the ZC, train movement authorization information, fault alarm information of the ZC, board card running state information of the ZC, or the like.
In the embodiment, the intelligent operation and maintenance system can monitor the running state of the ZC in real time after periodically receiving the maintenance data of the ZC.
In some embodiments, when the maintenance data includes alarm information, the type of a fault corresponding to the alarm information and a cause of the fault may be determined according to expert experience.
In this embodiment, when a new fault type occurs during the operation of the ZC and the fault type and the cause of the fault cannot be estimated from the existing expert experience, the new fault type needs to be identified or predicted by analyzing the intrinsic relation of the fault data using a machine learning model or a deep learning model.
And 120, under the condition that the maintenance data comprise alarm information, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on sample fault data and an online learning algorithm.
It can be understood that, when a fault occurs in the operation process of the ZC, the ZC maintenance data received by the intelligent operation and maintenance system usually includes corresponding alarm information for reminding maintenance personnel that the currently received maintenance data is abnormal.
In this step, the fault diagnosis model may be a deep learning model, for example, a sensor for online learning, or a machine learning model.
In this step, the sample fault data may be fault data that has occurred, for example, ZC fault data including information of faulty equipment, fault name, fault class, fault occurrence time information, and the like.
In this step, the diagnostic result corresponding to the maintenance data may include a fault type that has occurred in history, and may be processed by a fault diagnosis scheme stored in history, or may include a new fault type, and when there is no existing diagnostic scheme for the new fault type, the diagnostic result may be sent to a display screen for display, so that the operation and maintenance personnel may perform analysis and processing.
In this embodiment, when the diagnosis result output by the fault diagnosis model includes a fault type that has occurred in history, for example, the diagnosis result is "after route locking, signal is not opened", and the corresponding diagnosis scheme may be: 1. maintenance personnel: a temporary treatment mode (rapidly recovering the driving organization) and a final treatment mode (replacing equipment at night to complete the repair); 2. the dispatching personnel: the driving method is organized before the fault is recovered and the normal driving method is recovered after the maintenance personnel are repaired.
In this embodiment, the online learning algorithm may be a perceptron-aware algorithm, or may be a Passive-Aggressive algorithm (Passive-Aggressive) or the like.
According to the fault diagnosis method provided by the embodiment of the invention, the fault types contained in the ZC maintenance data are classified by using the fault diagnosis model obtained by the online learning algorithm, so that the unknown fault types are predicted, the fault diagnosis efficiency is improved, and the time cost and the labor cost are saved.
In some embodiments, the maintenance data includes running state information of the ZC, train movement authorization information, fault alarm information of the ZC, and board running state information of the ZC, and after receiving the maintenance data transmitted by the ZC, the method further includes: retrieving in a preset knowledge base by taking at least one of the running state information of the ZC, the train moving authorization information, the fault alarm information of the ZC and the board card running state information of the ZC as a retrieval condition to obtain a diagnosis result corresponding to the maintenance data; wherein the knowledge base is determined based on the existing fault data and the diagnostic scheme corresponding to the existing fault data.
In this embodiment, the knowledge base is a repository created in conjunction with expert experience and knowledge in handling different types of faults.
In this embodiment, the knowledge base may be a fault tree formed by a plurality of pieces of knowledge and association relations between different pieces of knowledge.
In this embodiment, the conclusion knowledge in the knowledge base has a hierarchical relationship specific to the fault tree, i.e., one conclusion knowledge in the knowledge base may be a prerequisite for another conclusion knowledge.
In this embodiment, the knowledge for processing different types of fault data is a node of a knowledge tree, and the connection line between two adjacent nodes may be a logical inference relationship between different kinds of knowledge, for example, a certain inference logic manner of the connection line between the node a and the node B may be a fault source and a fault reason corresponding to different kinds of knowledge through the logical inference manner.
In the embodiment, the retrieval in the knowledge base by using the retrieval condition is to simulate the thinking reasoning process of human experts by using experience and special knowledge accumulated by field experts for many years, and the experience knowledge has certain uncertainty.
According to the fault diagnosis method provided by the embodiment of the invention, the maintenance data of the ZC are sent to the knowledge base for quick matching, when the fault type of the maintenance data has corresponding knowledge in the knowledge base, the fault type and the fault solution can be directly determined from the knowledge base, so that the fault occurring in the operation of the ZC can be dealt with in time, and the safety is improved.
In some embodiments, the online learning algorithm is a perceptron algorithm, and the fault diagnosis model is obtained by: acquiring sample fault data of a ZC, wherein the sample fault data comprises various fault classification information; obtaining fault characteristics based on various fault classification information; and inputting the fault characteristics into the online learning model, and training the online learning model through a perceptron algorithm to obtain a fault diagnosis model.
In this embodiment, the online learning algorithm may be a perceptron algorithm, which is represented by an empirical data correlation matrix, which approximates a maximum edge hyperplane and is applicable to linearly separable data.
In other embodiments, the online learning algorithm may also be a passive attack algorithm, which has the same computational complexity as the sensor algorithm, but the fault diagnosis model obtained based on the passive attack algorithm has a stronger ability to identify fault information in the maintenance data than the fault diagnosis model obtained based on the sensor algorithm.
In this embodiment, the steps of constructing the fault diagnosis model are as follows:
(1) Sample fault data are obtained from historical fault data of the ZC system, and a sample label is set for each sample fault data.
In this embodiment, the fault type corresponding to the historical fault data may be wireless communication delay, delay between the ZC and an adjacent ZC, no train movement authorization, incomplete head screening, a special control command, a board crash, an illegal location report, and the like.
In this embodiment, the content of the sample label may define the setting more than the actual requirement of the user, for example, the sample label may be the number "0, 1, 2 …", or may be other character types besides the number.
(2) And extracting fault characteristics from the sample fault data, wherein the fault characteristics can be fault equipment, fault names, fault levels, fault contents, fault occurrence time and the like contained in the sample fault data.
In some embodiments, before extracting the fault feature, the validity of the sample fault data is determined, for example, whether the fault information includes specific information that points to the fault information, such as a faulty device, a fault name, and a fault level, and if the sample fault data is content that is not related to the specific information, the sample fault data is determined as illegal data.
(3) And training the online learning model by taking the fault characteristics as input characteristics and a perceptron algorithm as a training algorithm to obtain a fault diagnosis model, and monitoring the fault in real time by taking the fault diagnosis model as a classifier to obtain a classification result.
It should be noted that, compared with a batch learning method, an online learning algorithm is more suitable for processing fault feature information, and the online learning algorithm is an incremental learning algorithm, and is trained by using one sample each time, and then a weight vector is adjusted according to loss until an available fault diagnosis model is obtained.
According to the fault diagnosis method provided by the embodiment of the invention, the fault characteristics are obtained from the sample fault data, and the on-line learning model is trained by combining the sensor algorithm to obtain the fault diagnosis model, so that the fault identification efficiency and accuracy of the maintenance data are improved.
In some embodiments, the multiple fault classification information includes fault devices, fault names, fault levels, fault contents, fault occurrence times, and fault occurrence intervals, and the fault characteristics are obtained based on the multiple fault classification information, including: and respectively converting the various fault classification information into target character strings, and combining the target character strings according to a preset arrangement sequence to obtain a feature vector corresponding to the fault feature.
In this embodiment, the target character string can be customized according to actual requirements, for example, the target character string can be a number, a letter or other types of characters.
In this embodiment, the arrangement sequence may be set in a customized manner according to actual requirements.
In this embodiment, the multiple types of fault classification information are respectively extracted into target character strings, and are arranged and combined according to a preset arrangement order to obtain a feature vector, where the feature vector may be used to represent fault features.
In this embodiment, each target character string may be used as a feature and assigned a unique code, and the feature value is expressed by a boolean type, for example, the feature is represented by 1 and is not represented by 0; and the mapping relation between the features and the codes is represented by a map container, the feature character strings are inquired in the container, and if the feature character strings do not exist, the new feature character strings and the codes are added into the map container.
According to the fault diagnosis method provided by the embodiment of the invention, the target character strings corresponding to various fault classification information are extracted and combined into the feature vector, so that the characterization capability of fault features on fault types is improved, and the identification accuracy of the fault diagnosis model obtained by the fault features is improved.
In some embodiments, after obtaining the diagnosis result corresponding to the fault type, the method further includes: and displaying the diagnosis result on a display screen of the intelligent operation and maintenance system.
In the embodiment, when the intelligent operation and maintenance system diagnoses the ZC maintenance data, when the diagnosis result is a fault type which has occurred historically, the fault type can be processed through a fault diagnosis scheme which is stored historically, and the diagnosis result and the processing mode are displayed visually, so that operation and maintenance personnel can guide the maintenance operation of the field equipment according to the prompt content of the display screen.
In this embodiment, when the diagnosis result is a new fault type for which there is no existing diagnosis solution, the diagnosis result may be sent to be visualized for analysis and processing by the operation and maintenance personnel.
According to the fault diagnosis method provided by the embodiment of the invention, the diagnosis result is visually displayed, so that operation and maintenance personnel can conveniently execute operation and maintenance operation in time according to the prompt content of the display screen, the operation and maintenance safety of the ZC is improved, meanwhile, the maintenance time can be reduced, and the time cost and the labor cost are saved.
The following describes the fault diagnosis apparatus provided by the present invention, and the fault diagnosis apparatus described below and the fault diagnosis method described above may be referred to in correspondence with each other.
Fig. 2 is a schematic structural diagram of a fault diagnosis apparatus provided by the present invention, and as shown in fig. 2, the present invention further provides a fault diagnosis apparatus, which includes an obtaining module 210 and a diagnosis module.
An obtaining module 210, configured to obtain maintenance data of a ZC;
and the diagnosis module 220 is configured to, when it is determined that the maintenance data includes the alarm information, obtain a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model, where the diagnosis result includes a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on an online learning algorithm.
According to the fault diagnosis device provided by the embodiment of the invention, the fault types contained in the ZC maintenance data are classified by using the fault diagnosis model obtained by using the online learning algorithm, so that the unknown fault types are predicted, the fault diagnosis efficiency is improved, and the time cost and the labor cost are saved.
Fig. 3 is a second schematic structural diagram of the fault diagnosis device provided by the present invention, and as shown in fig. 3, the fault diagnosis device provided by the present invention further includes:
and a display module 230, configured to display the diagnosis result after obtaining the diagnosis result corresponding to the fault type.
According to the fault diagnosis device provided by the embodiment of the invention, the diagnosis result is visually displayed, so that operation and maintenance personnel can conveniently execute operation and maintenance operation in time according to the prompt content of the display screen, the operation and maintenance safety of the ZC is improved, the maintenance time can be reduced, and the time cost and the labor cost are saved.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a fault diagnosis method comprising: the method comprises the steps of obtaining maintenance data of a ZC, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprise alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on an online learning algorithm.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes 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 steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer-readable storage medium, the computer program, when executed by a processor, being capable of executing the fault diagnosis method provided by the above methods, the method including: the method comprises the steps of obtaining maintenance data of a ZC, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprise alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on an online learning algorithm.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing a fault diagnosis method provided by the above methods, the method including: the method comprises the steps of obtaining maintenance data of a ZC, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprise alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on an online learning algorithm.
The above-described embodiments of the apparatus are merely illustrative, and 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, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A fault diagnosis method is applied to an intelligent operation and maintenance system and is characterized by comprising the following steps:
acquiring maintenance data of a zone controller ZC;
and under the condition that the maintenance data comprise alarm information, obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model, wherein the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on sample fault data and an online learning algorithm.
2. The fault diagnosis method according to claim 1, wherein the maintenance data includes operation state information of the ZC, train movement authorization information, fault alarm information of the ZC, and board card operation state information of the ZC, and after the receiving of the maintenance data transmitted by the ZC, the method further comprises:
retrieving in a preset knowledge base by taking at least one of the running state information of the ZC, the train moving authorization information, the fault alarm information of the ZC and the board card running state information of the ZC as a retrieval condition to obtain a diagnosis result corresponding to the maintenance data;
wherein the knowledge base is determined based on the existing fault data and a diagnostic scheme corresponding to the existing fault data.
3. The fault diagnosis method according to claim 1, wherein the online learning algorithm is a perceptron algorithm, and the fault diagnosis model is obtained by the following steps:
acquiring sample fault data of the ZC, wherein the sample fault data comprises various fault classification information;
obtaining fault characteristics based on the multiple fault classification information;
inputting the fault characteristics into an online learning model, and training the online learning model through the perceptron algorithm to obtain the fault diagnosis model.
4. The fault diagnosis method according to claim 3, wherein the plurality of fault classification information includes fault equipment, fault names, fault levels, fault contents, fault occurrence times, and fault occurrence intervals, and the obtaining the fault characteristics based on the plurality of fault classification information includes:
and respectively converting the various fault classification information into target character strings, and combining the target character strings according to a preset arrangement sequence to obtain a feature vector corresponding to the fault feature.
5. The fault diagnosis method according to claim 1 or 2, wherein after the obtaining of the diagnosis result corresponding to the fault type, the method further comprises:
and displaying the diagnosis result on a display screen of the intelligent operation and maintenance system.
6. A failure diagnosis device characterized by comprising:
an acquisition module for acquiring maintenance data of a zone controller ZC;
the diagnosis module is used for obtaining a diagnosis result corresponding to the maintenance data based on a preset fault diagnosis model under the condition that the maintenance data comprises alarm information, the diagnosis result comprises a fault type corresponding to the alarm information, and the fault diagnosis model is obtained based on sample fault data and an online learning algorithm.
7. The fault diagnosis device according to claim 1, characterized in that the device further comprises:
and the display module is used for displaying the diagnosis result after the diagnosis result corresponding to the fault type is obtained.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the fault diagnosis method according to any one of claims 1 to 5 when executing the program.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the fault diagnosis method according to any one of claims 1 to 5.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the method of fault diagnosis according to any one of claims 1 to 5 when executed by a processor.
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