CN113610236A - Mine electromechanical equipment fault diagnosis support system based on crowd sensing - Google Patents
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Abstract
The invention discloses a mine electromechanical equipment fault diagnosis support system based on crowd sensing, which is characterized by comprising a fault request processing module, a fault preliminary diagnosis module, an expert diagnosis module and a maintenance scheduling module. The invention can standardize and standardize the fault library and the fault processing flow of the electromechanical equipment of the coal mine enterprise, guides and trains maintainers through the deposited equipment fault knowledge base, standardizes the overhaul site and really performs on-site 5S management; electromechanical equipment and an equipment fault library in the existing ERP of a coal mine enterprise are fully integrated with produced PLC data/production scheduling data, and the research is the application of data fusion and data deepening which are built at present, but not the informatization repeated construction.
Description
Technical Field
The invention relates to the technical field of mine electromechanical equipment fault diagnosis, in particular to a mine electromechanical equipment fault diagnosis support system based on crowd sensing.
Background
In the case that the information security requirements of the current country are more and more strict, it is becoming more and more infeasible to provide auxiliary diagnosis only by the conventional fault diagnosis method or by providing a data sharing mode through a browser (WEB mode).
Due to the technical condition limitation and the incomplete monitoring system, fault information transmission distortion exists in the management and maintenance of electromechanical equipment in the coal mine enterprises at present, the equipment has complicated and difficult faults, a maintenance site cannot obtain expert technical support at the first time, the conventional fault information is lack of digital precipitation, large data of the equipment is not formed, and the like.
In addition, at present, an ERP system or a PLC system is generally implemented by coal mine enterprises, the ERP system is mainly used for production management of the coal mine enterprises, and the PLC system is mainly used for production control of coal mine equipment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a mine electromechanical equipment fault diagnosis support system based on crowd sensing, which can standardize and standardize a fault library and a fault processing flow of electromechanical equipment of a coal mine enterprise, guides and trains maintainers through a deposited equipment fault knowledge base, standardizes a maintenance site and really performs on-site 5S management; the full sharing of the equipment failure knowledge base and the technician capability of the coal mine enterprise can be completely realized; the real-time communication channel between the equipment site and an industry expert is opened, the regional barriers of related participants of the faults can be eliminated through the current mobile internet technology, and the purposes of quick defect determination and timely maintenance of the faults are really realized; electromechanical equipment and an equipment fault library in the existing ERP of a coal mine enterprise are fully integrated with produced PLC data/production scheduling data, and the research is the application of data fusion and data deepening which are built at present, but not the informatization repeated construction.
In order to achieve the purpose, the invention adopts the following technical scheme:
the embodiment of the invention provides a mine electromechanical equipment fault diagnosis support system based on crowd sensing, which comprises a fault request processing module, a fault preliminary diagnosis module, an expert diagnosis module and a maintenance scheduling module;
the fault request processing module is used for receiving a fault processing request sent by an inspection/production person, the fault processing request at least comprises equipment abnormal information uploaded by the inspection/production person, and the fault request processing module carries out format standardization processing on the equipment abnormal information and then sends the equipment abnormal information to the fault primary diagnosis module;
the fault preliminary diagnosis module is used for preliminarily diagnosing the fault type corresponding to the equipment abnormal information by combining the fault knowledge base, evaluating the reliability of the diagnosis result, generating a corresponding maintenance work order by combining the fault type and the evaluation result and sending the corresponding maintenance work order to the maintenance scheduling module, or sending the equipment abnormal information and the preliminary diagnosis result to the expert diagnosis module;
the expert diagnosis module is used for selecting one or more cloud experts according to the equipment abnormal information and the preliminary diagnosis result, further diagnosing the fault type corresponding to the equipment abnormal information, generating a corresponding maintenance work order according to the diagnosis result and sending the maintenance work order to the maintenance scheduling module;
the maintenance scheduling module is connected with the coal mine production management system, the maintenance scheduling module receives and analyzes the maintenance work order, a maintenance flow comprising a plurality of scheduling instructions is generated, the scheduling instructions are sequentially sent to the coal mine production management system, the coal mine production management system creates a spare part work order required by the maintenance flow according to the scheduling instructions, maintenance personnel are arranged to execute the maintenance flow, a maintenance result is fed back to the fault request processing module, and the patrol inspection/production personnel judge whether to finish the fault processing request.
Optionally, the device abnormality information at least includes device description information and fault description information, and part of the device abnormality information further includes diagnosis feedback information.
Optionally, the device exception information includes three types of files: the fault signals generated in the specified format cannot be converted into fault maps, equipment pictures, equipment audios and videos of discrete signals and document records related to equipment operation.
Optionally, the process that the fault preliminary diagnosis module is configured to preliminarily diagnose the fault type corresponding to the device abnormal information by combining with the fault knowledge base, and evaluate the reliability of the diagnosis result includes the following steps:
analyzing the abnormal information of the equipment, and extracting a certain amount of fault keywords;
and generating a plurality of search formulas by combining the fault keywords and the fault keyword combination, searching the fault knowledge base for a plurality of times to obtain a diagnosis result, and evaluating the reliability of the diagnosis result according to the search formula corresponding to the diagnosis result.
Optionally, the fault request processing module includes a request presentation unit, a request modification unit and a diagnosis result processing unit;
the request proposing unit is used for proposing a fault processing request; the request modification unit is used for modifying the fault processing request; the diagnosis result processing unit is used for displaying the diagnosis result, selecting and modifying the fault processing request according to the satisfaction degree of the inspection/production personnel to request fault diagnosis again, or ending the fault diagnosis process, and integrating all process files generated by the diagnosis process into diagnosis cases to be stored in the fault knowledge base.
Optionally, the expert diagnosis module comprises a diagnosis mode judging unit, a self-selection diagnosis unit, a request center, an expert consultation platform and a remote diagnosis unit;
the diagnosis mode judging unit is used for identifying the fault processing request and judging whether the fault processing request meets the optional condition, if so, the fault processing request is sent to the optional diagnosis unit, otherwise, the fault processing request is sent to the request center;
the self-selection diagnosis unit is used for processing according to the fault request to generate an expert list, feeding the expert list back to the inspection/production personnel, and sending a diagnosis request to one of the inspection/production personnel or the expert according to the selection of the inspection/production personnel; the request center is used for selecting one or more cloud experts to directly send a diagnosis request according to the fault request;
when the diagnosis request is accepted by only one expert, starting the remote diagnosis unit, pushing the equipment abnormal information and the preliminary diagnosis result to the expert, and further diagnosing the fault type corresponding to the equipment abnormal information; and when the diagnosis request is accepted by a plurality of experts, starting the expert consultation platform, creating a consultation inlet, enabling the plurality of experts to enter a corresponding consultation room through the consultation inlet, and carrying out consultation on the fault type corresponding to the equipment abnormal information.
Optionally, the retrieval scheduling module judges the type of the fault diagnosis result, analyzes the professional type of the needed maintainer and the workpiece list required for maintenance, and sends the analysis result to the coal mine production management system;
and the coal mine production management system generates a maintenance work order and a spare part work order according to the professional type of the maintainer corresponding to the received fault diagnosis result and the online states of all the maintainers.
Optionally, the diagnosis support system further comprises a user management module, wherein the user management module is configured to manage identity types of the registered user, and the identity types include inspection personnel, production personnel, experts and overhaul personnel.
The invention has the beneficial effects that:
(1) when the invention solves the fault, all the information of the electromechanical equipment is packaged, and the packaged data comprises: all basic files of the equipment, historical fault information of the equipment, all real-time/historical operation parameter information of the equipment, operation parameter information of equipment related to the equipment, cracking analysis information of the equipment, and field pictures and video information when the equipment fault occurs, wherein after the information is subjected to intelligent pre-diagnosis or remote diagnosis, a formed solution is subjected to standardized classification according to the fault type and then is filed in a fault knowledge base, so that the standardization of a fault base of the elevator is realized.
(2) The mine electromechanical equipment fault diagnosis support system based on crowd sensing is communicated with a coal mine production management system, a solution formed by intelligent pre-diagnosis or remote diagnosis can continuously execute an actual maintenance process through ERP/EAM, functions of working tickets, operation tickets, spare part verification and the like are provided in the ERP/EAM, field technicians can operate according to a maintenance standard flow provided in the fault solution, and all maintenance flows are digitally filed.
(3) According to the mine electromechanical equipment fault diagnosis system, the operation state of the mine electromechanical equipment is comprehensively and effectively monitored, a large electromechanical equipment fault data packet is generated, and the system pushes equipment fault data to universities and colleges experts, equipment manufacturers and brother unit experts, so that industry experts in all regions can collect a unified cloud platform to perform accurate remote consultation on the fault, and an accurate solution is provided for eliminating the equipment fault.
(4) Aiming at the condition that the requirement of the current country on informatization security is more and more strict, the situation that the provision of auxiliary diagnosis is provided only by a traditional fault diagnosis method or a data sharing mode provided by a browser (WEB mode) becomes more and more infeasible, and the security guarantee mechanism of the project data is comprehensively considered from three aspects of a network topological graph, a data transmission mechanism and data encryption, so that the production data of the coal mine enterprises is ensured not to be intercepted by a third party privately.
(5) The invention organically integrates the production management level of the coal mine enterprise, saves the system investment cost and the fund utilization rate by combining the actual requirements of the coal mine enterprise, and gets through the information island in the enterprise.
(6) The invention aims at the short board in which the fault processing process of the traditional electromechanical equipment is decoupled from the enterprise resource management, the short board is connected with the enterprise ERP through the fault diagnosis support system of the mine electromechanical equipment, data is fully fused and shared, the equipment diagnosis process and the processing process are executed through the enterprise ERP, and the short board has the functions of issuing a fault processing work ticket and an operation ticket, verifying spare parts and the like in the ERP system.
Drawings
Fig. 1 is a schematic structural diagram of a mine electromechanical device fault diagnosis support system based on crowd sensing according to an embodiment of the present invention.
Fig. 2 is a schematic working flow diagram of a mine electromechanical equipment fault diagnosis support system according to an embodiment of the present invention.
Fig. 3 is a diagram illustrating functions related to registering a user according to an embodiment of the present invention.
Fig. 4 is a diagram illustrating the related functions of the expert and the user according to the embodiment of the present invention.
FIG. 5 is a diagram illustrating functions related to an administrator user according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a consultation process of experts according to an embodiment of the invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
Fig. 1 is a schematic structural diagram of a mine electromechanical device fault diagnosis support system based on crowd sensing according to an embodiment of the present invention. The diagnosis support system comprises a fault request processing module, a fault preliminary diagnosis module, an expert diagnosis module and a maintenance scheduling module.
The fault request processing module is used for receiving a fault processing request sent by an inspection/production person, the fault processing request at least comprises equipment abnormal information uploaded by the inspection/production person, and the fault request processing module carries out format standardization processing on the equipment abnormal information and then sends the equipment abnormal information to the fault primary diagnosis module.
The fault preliminary diagnosis module is used for preliminarily diagnosing the fault type corresponding to the equipment abnormal information by combining the fault knowledge base, evaluating the reliability of the diagnosis result, generating a corresponding maintenance work order by combining the fault type and the evaluation result and sending the corresponding maintenance work order to the maintenance scheduling module, or sending the equipment abnormal information and the preliminary diagnosis result to the expert diagnosis module.
The expert diagnosis module is used for selecting one or more cloud experts according to the equipment abnormal information and the preliminary diagnosis result, further diagnosing the fault type corresponding to the equipment abnormal information, generating a corresponding maintenance work order according to the diagnosis result and sending the maintenance work order to the maintenance scheduling module.
The maintenance scheduling module is connected with the coal mine production management system, the maintenance scheduling module receives and analyzes the maintenance work order, a maintenance flow comprising a plurality of scheduling instructions is generated, the scheduling instructions are sequentially sent to the coal mine production management system, the coal mine production management system creates a spare part work order required by the maintenance flow according to the scheduling instructions, maintenance personnel are arranged to execute the maintenance flow, a maintenance result is fed back to the fault request processing module, and the patrol inspection/production personnel judge whether to finish the fault processing request.
Fig. 2 is a schematic working flow diagram of a mine electromechanical equipment fault diagnosis support system according to an embodiment of the present invention. In combination with coal mine production practice, the support system relates to various types of users, and can be divided into the following types in a summary manner: enterprise technicians and managers requesting resolution (hereinafter referred to as users), remote diagnostics experts providing services, and system administrators performing task scheduling and coordination. The three user functions are mutually interlaced, and how to cooperate is one of the key technologies related to the system practicability and the development success or failure.
1. Diagnostic workflow
The mine electromechanical equipment fault diagnosis support system based on the crowd sensing takes a diagnosis request as a core, and diagnoses through a mode of interaction between a user and the system and between the user and an expert. The user puts forward a diagnosis request, intelligent diagnosis or expert diagnosis is carried out through the support system, if the system fault knowledge base has no fault data of similar types or the user is unsatisfied with a diagnosis conclusion, the user can further initiate expert diagnosis work, the system provides a fault data packet for an expert, the expert or an expert consultation group carries out real-time online diagnosis and gives a processing suggestion, and meanwhile, if the support system is accessed to an enterprise resource management (ERP), full-flow standardized management of equipment fault diagnosis and maintenance can be carried out.
The diagnosis support system further comprises a user management module for managing the identity types of the registered users, wherein the identity types comprise inspection personnel, production personnel, experts and overhaul personnel.
2. User function flow
The fault request processing module comprises a request proposing unit, a request modifying unit and a diagnosis result processing unit. The request proposing unit is used for proposing a fault processing request; the request modification unit is used for modifying the fault processing request; the diagnosis result processing unit is used for displaying the diagnosis result, selecting and modifying the fault processing request according to the satisfaction degree of the inspection/production personnel to request fault diagnosis again, or ending the fault diagnosis process, and integrating all process files generated by the diagnosis process into diagnosis cases to be stored in the fault knowledge base.
The remote diagnosis center gives different authorities to users according to different roles, and registered users of the system have the right to provide diagnosis requests to a system platform or remote experts. Referring to fig. 3, registered users have three main functions in the troubleshooting service: make a diagnosis request, modify a diagnosis request, and view a diagnosis result.
The registered user makes a diagnosis request to the system according to the equipment fault in the coal mine. Basic information of a diagnosis request is filled in firstly, and the basic information comprises information such as description of a failed device, description of the failure and recommendation of diagnosis, and resources related to the failure are uploaded to a system. The system divides the fault resources into three categories: the first is a signal file, namely a fault signal generated in a system specified format; secondly, pictures, videos and audios, namely fault maps and equipment pictures or videos and audios and the like which cannot be converted into discrete signals; and thirdly, documents, namely document records and the like related to the operation of the equipment. After the user uploads the relevant information, the request is submitted to the system, and the system automatically submits the request to an administrator for processing. After submitting the request, the user may also modify the content of the request before the administrator assigns the request to the relevant expert. After the expert concludes on the request made by the user, the user can view the results of the request and verify the accuracy of the results. If the conclusion does not meet the user's requirements, the user can further make a new request and the expert should continue to make a diagnosis for the user until the user is satisfied.
In this embodiment, the failure preliminary diagnosis module is configured to perform preliminary diagnosis on a failure type corresponding to the device abnormal information by combining with a failure knowledge base, and evaluate the reliability of a diagnosis result. On one hand, the setting can automatically screen out faults with obvious characteristics through the system, and the diagnosis burden of experts is reduced; on the other hand, for some faults with unobvious characteristics, some historical story cases can be provided for experts to refer to. Illustratively, the automatic diagnostic process of the preliminary fault diagnosis module includes the steps of: analyzing the abnormal information of the equipment, and extracting a certain amount of fault keywords; and generating a plurality of search formulas by combining the fault keywords and the fault keyword combination, searching the fault knowledge base for a plurality of times to obtain a diagnosis result, and evaluating the reliability of the diagnosis result according to the search formula corresponding to the diagnosis result. In practical applications, we can set: the more fault keywords that are satisfied, the higher the confidence level of the retrieved diagnosis results, and so on.
3. Diagnosis process of expert in diagnosis
Expert diagnostics are a core part of interactive diagnostic services. The remote diagnosis expert makes a diagnosis conclusion for the user by using the self equipment fault diagnosis experience and the diagnosis method provided by the system according to the request provided by the user.
Referring to fig. 4, the expert may enter the diagnostic flow in two ways to view new diagnostic requests and view accepted diagnostic requests. The former for those new diagnostic requests assigned by the diagnostic system administrator and the latter for requests that have been accepted but not yet diagnosed.
When a new diagnostic request is received, the expert can review the basic information of the request and then decide whether to accept or reject the request. If the request is denied, the system administrator will reassign the request to other experts; if the request is accepted, the remote diagnostic function is entered. The expert may then view the details of the diagnostic request, including the various diagnostic resources uploaded by the user.
The expert can diagnose each resource submitted by the user, give a corresponding diagnosis conclusion and fill the diagnosis report. And after the diagnosis is finished, sending a diagnosis report to the user, and waiting for further verification of the user. Every step of diagnosis made by the expert is recorded in a corresponding database and becomes a future diagnosis resource of the system. For user-provided signal resources, the system provides many common, advanced signal processing and analysis methods for experts.
4. Functional flow for diagnosing system administrators
Referring to fig. 5, the administrator of the diagnostic system is responsible for checking the diagnostic request of the user and accordingly changing the status of the request to sent. In addition, the administrator is also responsible for making corresponding processing to the request for becoming a system user or expert. The administrator needs to verify the identity of the user or the qualification of the expert, and the qualified user or expert can become a registered user or expert of the system.
5. Expert consultation process
Referring to fig. 6, the expert diagnosis module includes a diagnosis manner discriminating unit, an optional diagnosis unit, a request center, an expert consultation platform, and a remote diagnosis unit. The diagnosis mode judging unit is used for identifying the fault processing request and judging whether the fault processing request meets the optional condition, if so, the fault processing request is sent to the optional diagnosis unit, otherwise, the fault processing request is sent to the request center; the self-selection diagnosis unit is used for processing according to the fault request to generate an expert list, feeding the expert list back to the inspection/production personnel, and sending a diagnosis request to one of the inspection/production personnel or the expert according to the selection of the inspection/production personnel; the request center is used for selecting one or more cloud experts to directly send a diagnosis request according to the fault request; when the diagnosis request is accepted by only one expert, starting the remote diagnosis unit, pushing the equipment abnormal information and the preliminary diagnosis result to the expert, and further diagnosing the fault type corresponding to the equipment abnormal information; and when the diagnosis request is accepted by a plurality of experts, starting the expert consultation platform, creating a consultation inlet, enabling the plurality of experts to enter a corresponding consultation room through the consultation inlet, and carrying out consultation on the fault type corresponding to the equipment abnormal information.
Expert consultation this concept comes from the medical field and aims to collect the knowledge and experience of many experts in the field and solve some complex and critical problems. It is just the embodiment of the maximum diagnostic resource sharing and interoperability idea proposed by the remote diagnostic system.
The initiator of the expert consultation is also a registered user, and the registered user determines the list of the experts participating in the expert consultation according to the request and sends the request to the experts. Any domain-oriented expert is commissioned as a group leader among these experts. The experts obtain respective diagnosis conclusions through analysis according to the request information provided by the user through a diagnosis method provided by the system. In the process, experts can communicate through an interactive platform provided by the system. Finally, the conclusion of each expert is collected into the diagnosis team leader, and the team leader combines the diagnosis conclusion of the team leader to finally give a complete diagnosis report and send the complete diagnosis report to the user. Thus, one specialist consultation is completed.
The biggest advantage of expert consultation is that it can make full use of the experience of different experts, and give conclusions through mutual communication, thus avoiding one-sidedness caused by the independent undertaking of diagnosis tasks by experts in interactive diagnosis, and most accurately giving the cause of fault and the solution.
After the expert diagnoses, the maintenance personnel are required to be arranged to maintain the equipment. Illustratively, the retrieval scheduling module judges the type of the fault diagnosis result, analyzes the professional type of the needed maintainer and the workpiece list required by maintenance, and sends the analysis result to the coal mine production management system; and the coal mine production management system generates a maintenance work order and a spare part work order according to the professional type of the maintainer corresponding to the received fault diagnosis result and the online states of all the maintainers. The embodiment aims at the short board in which the conventional electromechanical equipment fault processing process and enterprise resource management are decoupled, the short board is connected with enterprise ERP through a mine electromechanical equipment fault diagnosis support system, data are fully fused and shared, the equipment diagnosis process and the processing process are executed through the enterprise ERP, and the functions of issuing a fault processing work ticket and an operation ticket, verifying spare parts and the like are provided in the ERP system. This embodiment is from promoting the production management level of coal mine enterprise, practicing thrift system input cost and fund utilization ratio angle, combines the actual demand of coal mine enterprise to carry out organic integration to both, makes through the inside "information isolated island" of enterprise.
In the embodiment, a mine electromechanical equipment fault diagnosis support system is researched and developed based on technologies such as popular cooperation of the internet, large-scale cooperation of expert knowledge resources, open sharing and the like, and the space-time barrier between an equipment field and an industry expert is opened. In the embodiment, an equipment fault library formed in the fault diagnosis and equipment maintenance of the mine electromechanical equipment is utilized, group expert resources and equipment fault knowledge base resources are fully shared, and a quick and accurate solution is provided for eliminating faults. The embodiment breaks through the multi-source heterogeneous electromechanical equipment working condition parameter acquisition method and the fusion technology. By utilizing an open data synchronization algorithm, multi-source heterogeneous data such as real-time working conditions, historical data, manual reports, videos and the like of the mine electromechanical equipment are collected, and fusion of various data is realized.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. A mine electromechanical equipment fault diagnosis support system based on crowd sensing is characterized in that the diagnosis support system comprises a fault request processing module, a fault preliminary diagnosis module, an expert diagnosis module and a maintenance scheduling module;
the fault request processing module is used for receiving a fault processing request sent by an inspection/production person, the fault processing request at least comprises equipment abnormal information uploaded by the inspection/production person, and the fault request processing module carries out format standardization processing on the equipment abnormal information and then sends the equipment abnormal information to the fault primary diagnosis module;
the fault preliminary diagnosis module is used for preliminarily diagnosing the fault type corresponding to the equipment abnormal information by combining the fault knowledge base, evaluating the reliability of the diagnosis result, generating a corresponding maintenance work order by combining the fault type and the evaluation result and sending the corresponding maintenance work order to the maintenance scheduling module, or sending the equipment abnormal information and the preliminary diagnosis result to the expert diagnosis module;
the expert diagnosis module is used for selecting one or more cloud experts according to the equipment abnormal information and the preliminary diagnosis result, further diagnosing the fault type corresponding to the equipment abnormal information, generating a corresponding maintenance work order according to the diagnosis result and sending the maintenance work order to the maintenance scheduling module;
the maintenance scheduling module is connected with the coal mine production management system, the maintenance scheduling module receives and analyzes the maintenance work order, a maintenance flow comprising a plurality of scheduling instructions is generated, the scheduling instructions are sequentially sent to the coal mine production management system, the coal mine production management system creates a spare part work order required by the maintenance flow according to the scheduling instructions, maintenance personnel are arranged to execute the maintenance flow, a maintenance result is fed back to the fault request processing module, and the patrol inspection/production personnel judge whether to finish the fault processing request.
2. The mining electromechanical device fault diagnosis support system based on crowd sensing according to claim 1, wherein the device abnormality information includes at least device description information and fault description information, and part of the device abnormality information further includes diagnosis feedback information.
3. The mining electromechanical device fault diagnosis support system based on crowd sensing according to claim 2, wherein the device abnormality information includes three types of files: the fault signals generated in the specified format cannot be converted into fault maps, equipment pictures, equipment audios and videos of discrete signals and document records related to equipment operation.
4. The mine electromechanical equipment fault diagnosis support system based on crowd sensing according to claim 3, wherein the fault preliminary diagnosis module is configured to preliminarily diagnose a fault type corresponding to the equipment abnormality information in combination with a fault knowledge base, and the process of evaluating the reliability of the diagnosis result includes the following steps:
analyzing the abnormal information of the equipment, and extracting a certain amount of fault keywords;
and generating a plurality of search formulas by combining the fault keywords and the fault keyword combination, searching the fault knowledge base for a plurality of times to obtain a diagnosis result, and evaluating the reliability of the diagnosis result according to the search formula corresponding to the diagnosis result.
5. The mining electromechanical device fault diagnosis support system based on crowd sensing according to claim 3, wherein the fault request processing module includes a request making unit, a request modifying unit, and a diagnosis result processing unit;
the request proposing unit is used for proposing a fault processing request; the request modification unit is used for modifying the fault processing request; the diagnosis result processing unit is used for displaying the diagnosis result, selecting and modifying the fault processing request according to the satisfaction degree of the inspection/production personnel to request fault diagnosis again, or ending the fault diagnosis process, and integrating all process files generated by the diagnosis process into diagnosis cases to be stored in the fault knowledge base.
6. The mine electromechanical equipment fault diagnosis support system based on crowd sensing according to claim 1, wherein the expert diagnosis module comprises a diagnosis mode discrimination unit, an optional diagnosis unit, a request center, an expert consultation platform and a remote diagnosis unit;
the diagnosis mode judging unit is used for identifying the fault processing request and judging whether the fault processing request meets the optional condition, if so, the fault processing request is sent to the optional diagnosis unit, otherwise, the fault processing request is sent to the request center;
the self-selection diagnosis unit is used for processing according to the fault request to generate an expert list, feeding the expert list back to the inspection/production personnel, and sending a diagnosis request to one of the inspection/production personnel or the expert according to the selection of the inspection/production personnel; the request center is used for selecting one or more cloud experts to directly send a diagnosis request according to the fault request;
when the diagnosis request is accepted by only one expert, starting the remote diagnosis unit, pushing the equipment abnormal information and the preliminary diagnosis result to the expert, and further diagnosing the fault type corresponding to the equipment abnormal information; and when the diagnosis request is accepted by a plurality of experts, starting the expert consultation platform, creating a consultation inlet, enabling the plurality of experts to enter a corresponding consultation room through the consultation inlet, and carrying out consultation on the fault type corresponding to the equipment abnormal information.
7. The mine electromechanical equipment fault diagnosis support system based on crowd sensing according to claim 1, wherein the retrieval scheduling module judges the type of the fault diagnosis result, analyzes the professional type of the required maintainer and the workpiece list required for maintenance, and sends the analysis result to a coal mine production management system;
and the coal mine production management system generates a maintenance work order and a spare part work order according to the professional type of the maintainer corresponding to the received fault diagnosis result and the online states of all the maintainers.
8. The mine electromechanical equipment fault diagnosis support system based on crowd sensing according to claim 1, wherein the diagnosis support system further comprises a user management module, the user management module is configured to manage identity types of registered users, and the identity types include inspection personnel, production personnel, experts and overhaul personnel.
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