CN111435366A - Equipment fault diagnosis method and device and electronic equipment - Google Patents
Equipment fault diagnosis method and device and electronic equipment Download PDFInfo
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- CN111435366A CN111435366A CN201910033313.XA CN201910033313A CN111435366A CN 111435366 A CN111435366 A CN 111435366A CN 201910033313 A CN201910033313 A CN 201910033313A CN 111435366 A CN111435366 A CN 111435366A
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000003672 processing method Methods 0.000 claims abstract description 29
- 238000012423 maintenance Methods 0.000 claims abstract description 22
- 238000000605 extraction Methods 0.000 claims description 23
- 238000010586 diagram Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 6
- 238000013024 troubleshooting Methods 0.000 claims description 5
- 208000037408 Device failure Diseases 0.000 claims description 3
- 230000007257 malfunction Effects 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
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- 238000006243 chemical reaction Methods 0.000 description 3
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- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
Abstract
The application provides a method, a device and an electronic device for diagnosing equipment faults, wherein when the equipment faults are detected, equipment information of the faulty equipment is obtained; and determining the fault reason of the fault equipment by using the equipment fault diagnosis knowledge graph. The equipment fault diagnosis knowledge map adopts an artificial intelligence algorithm, learns the summarized fault judgment logic relation from massive equipment fault maintenance reports, and excavates the fault reason, so that multi-factor complex faults can be diagnosed and investigated, and meanwhile, the equipment information of the fault equipment based on user feedback can be quickly (second speed) by means of the knowledge correlation relation of the knowledge map, and the fault reason and the fault processing method can be quickly and correctly diagnosed.
Description
Technical Field
The invention relates to the technical field of industrial equipment diagnosis, in particular to an equipment fault diagnosis method and device and electronic equipment.
Background
Along with the continuous promotion of industrialization degree, industrial equipment tends to be more intelligent, has greatly promoted production efficiency. However, the more precise and intelligent equipment has more complex structure, higher maintenance difficulty and higher requirement on experience of a maintenance master.
At present, the method for diagnosing equipment faults in the industry mainly comprises sensor equipment on-line monitoring and an expert diagnosis system, but the fault diagnosis analysis method which only relies on the sensor monitoring and the expert system has the problems of difficult summary of fault diagnosis experience, weak correlation judgment of fault diagnosis factors and the like.
Disclosure of Invention
In order to solve the above problems, the present invention provides a device fault diagnosis method, apparatus and electronic device, which can quickly diagnose a device fault and a fault processing method.
The application provides an equipment fault diagnosis method, which comprises the following steps:
when the equipment is detected to have a fault, acquiring equipment information of the faulty equipment;
and matching the equipment information of the fault equipment with an equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
Optionally, before matching the device information of the faulty device with the device fault diagnosis knowledge-graph, the method further includes:
extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
the historical equipment maintenance report comprises equipment information and fault reason information of fault equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
Optionally, before matching the device information of the faulty device with the device fault diagnosis knowledge-graph, the method further includes:
and extracting knowledge of the equipment information of the fault equipment to form structured data corresponding to the equipment information of the fault equipment.
Optionally, matching the device information of the faulty device with a device fault diagnosis knowledge graph, and determining a fault cause of the faulty device includes:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
Optionally, the apparatus fault diagnosis knowledge map further includes apparatus information of the faulty apparatus, association between fault cause information and fault processing method information, and then determining the fault cause of the faulty apparatus further includes:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
The present application further provides an apparatus for diagnosing a device failure, including:
the acquisition module is used for acquiring equipment information of the fault equipment when the equipment is detected to have faults;
and the matching module is used for matching the equipment information of the fault equipment with the equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
Optionally, the apparatus further comprises:
the knowledge extraction module is used for extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
the historical equipment maintenance report comprises equipment information and fault reason information of fault equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
Optionally, the knowledge extraction module is further configured to extract knowledge from the device information of the faulty device, so as to form structured data corresponding to the device information of the faulty device;
optionally, the matching module is specifically configured to:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
Optionally, the equipment fault diagnosis knowledge graph further includes an association relationship between the equipment information of the faulty equipment, the fault cause information, and the fault processing method information, and then the matching module is further configured to:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
The present application further provides an electronic device, comprising: a memory, a processor, and a communication component;
the memory for storing a computer program;
the processor is coupled with the memory and the communication component for executing a computer program for performing the above-described device fault diagnosis method.
The present application also provides a computer-readable storage medium storing a computer program which, when executed, is capable of implementing the apparatus fault diagnosis method.
According to the embodiment of the application, when the equipment is detected to have faults; acquiring equipment information of a fault equipment; and determining the fault reason of the fault equipment by using the equipment fault diagnosis knowledge graph. The equipment fault diagnosis knowledge map adopts an artificial intelligence algorithm, learns the summarized fault judgment logic relation knowledge map from massive equipment fault maintenance reports, and excavates the fault reason and the corresponding fault processing method, so that the diagnosis and the troubleshooting can be carried out on multi-factor complex faults, and meanwhile, the equipment information of the fault equipment based on user feedback can be quickly (second speed) and the fault reason and the fault processing method can be quickly and correctly diagnosed by means of the knowledge correlation of the knowledge map.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or 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 an apparatus fault diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus fault diagnosis device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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 terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and "a" and "an" generally include at least two, but do not exclude at least one, unless the context clearly dictates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
It should be noted that, in the embodiment of the present invention, the equipment fault diagnosis knowledge graph learns the summarized fault determination logic from a large number of equipment fault repair reports through a knowledge extraction function, and finds out the fault reason; it should be further noted that, in practical applications, in accordance with generation of new equipment maintenance report data, through a knowledge extraction function, the present embodiment needs to continuously learn and mine new equipment maintenance report data, learn summarized new fault judgment logic, and mine new fault reasons and fault processing methods, so as to continuously improve an equipment fault diagnosis knowledge graph, and therefore, diagnosis and troubleshooting can be performed on multi-factor complex faults, and meanwhile, by means of knowledge correlation of the knowledge graph, fault diagnosis reasons can be recommended quickly (at a second rate) based on operation parameters of fault equipment fed back by a user. For this reason, the knowledge extraction function needs to be explained.
The application can adopt a knowledge extraction system based on Natural L language Processing (N L P), which applies N L P technology to extract knowledge from the language segments of documents after the processes of word segmentation, part of speech tagging, slot filling, event extraction and the like, and then converts the sentences described by Natural language into a form understandable by a computer through structured data and stores the form into a knowledge map database.
The knowledge extraction is to extract knowledge from data of at least one source and at least one structure to form structured data of knowledge, and the structured data of the knowledge is stored in a knowledge graph.
The knowledge extraction system adopted by the application comprises the following steps: data conversion, ontology construction, knowledge extraction and the like.
Specifically, the data conversion is mainly used for collecting the corpus, the corpus extracted by the method is from a massive document set of 'equipment failure overhaul reports', and the massive 'equipment failure overhaul reports' are converted into a TXT document set of the equipment failure overhaul reports through the data conversion.
The body construction is that according to the information in the equipment fault maintenance report, according to the knowledge triple structure of the equipment fault diagnosis knowledge map (for example, the following knowledge triple structure: equipment, including, component > < component, having, index > < equipment, occurring, fault > < fault reason, resulting in, fault > < fault processing scheme, solving, fault >), the required knowledge is extracted, and organized in a certain semantic relationship and stored in a knowledge map database.
The knowledge extraction mainly adopts a natural language processing technology, and the knowledge extraction is carried out on the equipment fault maintenance report through processes of word segmentation, word list query, extraction rule definition, named entity identification, slot filling, event extraction and the like according to a specified sequence, so as to obtain structured data of a knowledge triple structure (equipment, including, part > < part, having, index > < equipment, occurrence, fault > < fault cause, resulting in, fault > < fault processing scheme, solution, fault >) of the equipment fault diagnosis knowledge graph.
Fig. 1 is a schematic flow chart of an apparatus fault diagnosis method according to an embodiment of the present invention, as shown in fig. 1:
101. when the equipment is detected to have a fault, acquiring equipment information of the faulty equipment;
for example, when the device monitoring system detects that a fault or an abnormality exists in the device, the device monitoring system may obtain device information of the device with the fault (referred to as a faulty device for short), where the device information includes an operating parameter of the faulty device.
102. And matching the equipment information of the fault equipment with an equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
Before step 102, the present application needs to extract knowledge from a large number of historical equipment overhaul reports by using a knowledge extraction method to generate an equipment fault diagnosis knowledge graph.
The historical equipment overhaul report comprises information of faulty equipment, fault reasons, fault maintenance records and the like, and a part of information is unstructured data (such as documents, texts, pictures, XM L, HTM L, various reports, images, audio/video information and the like), so that knowledge extraction needs to be carried out on the historical equipment overhaul report to generate an equipment fault diagnosis knowledge map.
The equipment fault diagnosis knowledge map comprises a fault judgment logic relation map, the fault judgment logic relation map comprises structured data of logic association relations between equipment information and fault reason information of fault equipment, and unstructured data in a historical equipment overhaul report are generated into corresponding structured data in the equipment fault diagnosis knowledge map through knowledge extraction.
Therefore, in the embodiment of the present application, in order to match with the device failure diagnosis knowledge graph and quickly diagnose the failure cause, knowledge extraction needs to be performed on the device information of the failed device to form corresponding structured data. And then, matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
It should be further noted that, in practical application, with the generation of new equipment maintenance report data, through the knowledge extraction function, the embodiment needs to continuously learn and mine new equipment maintenance report data, learn summarized new fault judgment logic, and mine new fault reasons and fault processing methods, so as to continuously perfect an equipment fault diagnosis knowledge graph; and semi-automatic and automatic updating and expansion can be performed on the map knowledge of the equipment fault diagnosis knowledge map through a knowledge reasoning rule method.
103. And determining fault processing method information corresponding to the fault reason information by using the equipment fault diagnosis knowledge map.
In this embodiment, the device fault diagnosis knowledge graph further includes an association relationship between the device information and the fault cause information of the faulty device and the fault processing method information, and specifically, the device fault diagnosis knowledge graph may be triple structure data (< device, including, component > < component, having, index > < device, occurring, fault > < fault cause, resulting, fault > < fault processing scheme, solution, fault >).
Further, after determining the failure cause of the failed device, the method further includes:
and determining fault processing method information corresponding to the fault reason information by using the association relationship (namely the triple structured data) among the equipment information, the fault reason information and the fault processing method information of the fault equipment in the equipment fault diagnosis knowledge map.
According to the embodiment of the application, when the equipment is detected to have faults; acquiring equipment information of a fault equipment; and determining the fault reason of the fault equipment by using the equipment fault diagnosis knowledge graph. The equipment fault diagnosis knowledge map adopts an artificial intelligence algorithm, learns the summarized fault judgment logic relation knowledge map from massive equipment fault maintenance reports, and excavates the fault reason and the corresponding fault processing method, so that the diagnosis and the troubleshooting can be carried out on multi-factor complex faults, and meanwhile, the equipment information of the fault equipment based on user feedback can be quickly (second speed) and the fault reason and the fault processing method can be quickly and correctly diagnosed by means of the knowledge correlation of the knowledge map.
Fig. 2 is a schematic structural diagram of an apparatus fault diagnosis device according to an embodiment of the present invention, as shown in fig. 2:
the acquisition module is used for acquiring equipment information of the fault equipment when the equipment is detected to have faults;
and the matching module is used for matching the equipment information of the fault equipment with the equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
Optionally, the apparatus further comprises:
the knowledge extraction module is used for extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
wherein the historical equipment overhaul report at least comprises equipment information and fault reason information of fault equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
Optionally, the knowledge extraction module is further configured to extract knowledge from the device information of the faulty device, so as to form structured data corresponding to the device information of the faulty device;
optionally, the matching module is specifically configured to:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
Optionally, the equipment fault diagnosis knowledge graph further includes an association relationship between the equipment information of the faulty equipment, the fault cause information, and the fault processing method information, and then the matching module is further configured to:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
The apparatus shown in this embodiment may perform the method embodiment shown in fig. 1, and the implementation principle and the technical effect are not described again.
Fig. 3 is a schematic structural diagram of an electronic device according to another embodiment of the present invention, as shown in fig. 3, including:
a memory, a processor, and a communication component;
a memory for storing a computer program;
a processor coupled with the memory and the communication component for executing a computer program for performing:
when the equipment is detected to have a fault, acquiring equipment information of the faulty equipment;
and matching the equipment information of the fault equipment with an equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
The processor is further configured to perform:
extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
the historical equipment maintenance report comprises equipment information and fault reason information of fault equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
The processor is further configured to perform:
and extracting knowledge of the equipment information of the fault equipment to form structured data corresponding to the equipment information of the fault equipment.
The processor is further configured to perform:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
The equipment fault diagnosis knowledge map further includes an association relationship between the equipment information of the faulty equipment, the fault cause information and the fault processing method information, and the processor is further configured to execute:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
Further, as shown in fig. 3, the electronic device further includes: display, power components, audio components, and the like. Only some of the components are schematically shown in fig. 3, and it is not meant that the electronic device comprises only the components shown in fig. 3.
The server shown in this embodiment may execute the method embodiment shown in fig. 1, and the implementation principle and technical effect thereof are not described again.
Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, the relevant steps or operations in the embodiment of the method shown in fig. 1 can be implemented, which is not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but 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 (12)
1. An apparatus fault diagnosis method, comprising:
when the equipment is detected to have a fault, acquiring equipment information of the faulty equipment;
and matching the equipment information of the fault equipment with an equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
2. The method of claim 1, wherein matching the device information of the malfunctioning device to a device troubleshooting knowledge-graph further comprises:
extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
wherein the historical equipment overhaul report comprises equipment information and fault reason information of the faulty equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
3. The method of claim 1, wherein matching the device information of the malfunctioning device to a device troubleshooting knowledge-graph further comprises:
and extracting knowledge of the equipment information of the fault equipment to form structured data corresponding to the equipment information of the fault equipment.
4. The method of claim 2 or 3, wherein matching the device information of the malfunctioning device to a device failure diagnosis knowledge-graph, determining the cause of the malfunction of the malfunctioning device comprises:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
5. The method according to claim 4, wherein the equipment fault diagnosis knowledge map further includes the equipment information of the faulty equipment, the association relationship between the fault reason information and the fault processing method information, and then determining the fault reason of the faulty equipment further includes:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
6. An apparatus for diagnosing a failure of a device, comprising:
the acquisition module is used for acquiring equipment information of the fault equipment when the equipment is detected to have faults;
and the matching module is used for matching the equipment information of the fault equipment with the equipment fault diagnosis knowledge graph to determine the fault reason of the fault equipment.
7. The apparatus of claim 6, further comprising:
the knowledge extraction module is used for extracting knowledge of historical equipment maintenance reports to generate the equipment fault diagnosis knowledge map;
the historical equipment maintenance report comprises equipment information and fault reason information of fault equipment;
the equipment fault diagnosis knowledge map comprises a fault judgment logic relation diagram, and the fault judgment logic relation diagram comprises equipment information of fault equipment and structured data of logic association relation between fault reason information.
8. The apparatus of claim 6, further comprising:
the knowledge extraction module is further configured to extract knowledge from the device information of the faulty device, and form structured data corresponding to the device information of the faulty device.
9. The apparatus of claim 8, wherein the matching module is specifically configured to:
and matching the structured data corresponding to the equipment information of the fault equipment with the equipment fault diagnosis knowledge map, and determining fault reason information corresponding to the equipment information of the fault equipment according to the association relationship between the equipment information of the fault equipment and the fault reason information.
10. The apparatus according to claim 8, wherein the device fault diagnosis knowledge graph further includes an association relationship between the device information of the faulty device, the fault cause information, and the fault handling method information, and then the matching module is further configured to:
and determining fault processing method information corresponding to the fault reason information by using the association relationship among the equipment information of the fault equipment, the fault reason information and the fault processing method information in the equipment fault diagnosis knowledge map.
11. An electronic device, comprising: a memory, a processor, and a communication component;
the memory for storing a computer program;
the processor is coupled with the memory and the communication component for executing a computer program for performing the method of any of claims 1-5.
12. A computer-readable storage medium, characterized in that a computer program is stored which, when executed, is capable of implementing the method of any one of claims 1-5.
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| CN111881278A (en) * | 2020-07-27 | 2020-11-03 | 珠海格力电器股份有限公司 | Fault confirmation method and device, storage medium, electronic device and air conditioner |
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| CN112596936A (en) * | 2020-12-04 | 2021-04-02 | 光大科技有限公司 | Method and device for determining system fault reason, storage medium and electronic device |
| CN112596495B (en) * | 2020-12-07 | 2022-03-25 | 中科蓝智(武汉)科技有限公司 | Industrial equipment fault diagnosis method and system based on knowledge graph |
| CN112596495A (en) * | 2020-12-07 | 2021-04-02 | 中科蓝智(武汉)科技有限公司 | Industrial equipment fault diagnosis method and system based on knowledge graph |
| CN112612904A (en) * | 2020-12-28 | 2021-04-06 | 交控科技股份有限公司 | Rail transit emergency method and device based on knowledge graph |
| CN112810772A (en) * | 2021-02-01 | 2021-05-18 | 江苏远望仪器集团有限公司 | Ship equipment fault diagnosis method and equipment based on multi-dimensional feature knowledge extraction |
| CN115184807A (en) * | 2022-07-05 | 2022-10-14 | 东莞新能安科技有限公司 | Battery fault detection method, device, equipment, medium and product |
| CN116009517A (en) * | 2023-01-18 | 2023-04-25 | 北京控制工程研究所 | Method and device for constructing performance-fault relation map of spacecraft control system |
| CN116009517B (en) * | 2023-01-18 | 2023-08-29 | 北京控制工程研究所 | Method and device for constructing performance-fault relation map of spacecraft control system |
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