CN116088469A - Expert system-based generalized fault diagnosis platform system - Google Patents
Expert system-based generalized fault diagnosis platform system Download PDFInfo
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- CN116088469A CN116088469A CN202211677680.1A CN202211677680A CN116088469A CN 116088469 A CN116088469 A CN 116088469A CN 202211677680 A CN202211677680 A CN 202211677680A CN 116088469 A CN116088469 A CN 116088469A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The invention also discloses a generalized fault diagnosis platform system based on the expert system, which comprises: the knowledge acquisition module is used for acquiring user facts from the train control system monitoring maintenance equipment; the knowledge base and the management system thereof are used for storing expert knowledge; the knowledge base management system is used for searching, organizing and maintaining expert knowledge in the knowledge base; the comprehensive database is used for receiving and storing user facts from the knowledge base and the management system thereof, and describing problems according to the user facts; the inference engine is used for processing the received problem description to obtain an intermediate result; the interpreter is used for processing the intermediate result according to the received operation information to obtain a final result; and the man-machine interface is used for outputting the final result to each maintenance subsystem APP in the train control system monitoring maintenance equipment. The invention can integrate all the trivial and single alarm information and carry out intelligent diagnosis, thereby improving the diagnosis efficiency and accuracy.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a generalized fault diagnosis platform system based on an expert system.
Background
With the rapid development of the train control system, the application of train control equipment is also endless: such as a train control center, a temporary speed limiting server, a wireless blocking center, an interval comprehensive monitoring device, a train control interlocking integrated device under a novel train control system and the like, which have maintenance diagnosis functions such as self diagnosis, fault alarm and the like, and mainly realize maintenance diagnosis measures through maintenance diagnosis software to display various record, monitoring, alarm and fault information.
The traditional maintenance terminal can also provide a fault alarm function, but the alarms are all information directly transmitted by a logic subsystem, an execution subsystem, an operation subsystem and the like, the alarm content is trivial, the source is single, and the intelligent maintenance diagnosis requirement is not met. Especially in the objective factors such as more complicated station type, unmanned station's management maintenance, the station scene that the debugging cost is higher, more strict debugging environment, just rely on traditional maintenance terminal, hardly realize quick location, maintenance efficiency is not high.
Disclosure of Invention
The invention aims to provide a generalized fault diagnosis platform system based on an expert system, which integrates trivial single alarm information by establishing a database and combining artificial intelligent computation such as a neural network and the like, thereby realizing accurate intelligent diagnosis of faults of each train control maintenance subsystem and outputting diagnosis results.
In order to achieve the above object, the present invention is realized by the following technical scheme:
an expert system-based generalized fault diagnosis platform system, comprising: a knowledge acquisition module 101, configured to acquire a user fact from the train control system monitoring maintenance device; the knowledge base and the management system 102 thereof are connected with the knowledge acquisition module 101; wherein the knowledge base is used for storing expert knowledge; the knowledge base management system is used for retrieving, organizing and maintaining the expert knowledge in the knowledge base; a comprehensive database 104 connected with the knowledge base and its management system 102, for receiving and storing the user facts from the knowledge base and its management system 102, and describing problems according to the user facts; the inference machine 103 is connected with the comprehensive database 104 and is used for processing the received problem description to obtain an intermediate result; an interpreter 105, which is respectively connected with the inference engine 103 and the comprehensive database 104, wherein the interpreter 105 is used for processing the intermediate result according to the received operation information to obtain a final result; and the man-machine interface 106 is connected with the interpreter 105 and is used for outputting the final result to each maintenance subsystem APP202 in the train control system monitoring maintenance equipment.
Optionally, the expert knowledge includes expert empirical knowledge, principle knowledge within the domain, and related facts.
Optionally, the knowledge acquisition module 101 is further configured to acquire expert knowledge and transmit the expert knowledge to the knowledge base and the management system 102 thereof.
Optionally, the knowledge base management system 102 is configured to retrieve, organize and maintain the expert knowledge in the knowledge base; specifically, expert knowledge is collected firstly, then organized according to a given data structure, consistency and integrity of logic before and after the expert knowledge is checked and maintained, and the checked expert knowledge is stored; the expert knowledge participating in the inference engine 103 is learned again, and if the original expert knowledge is limited according to the operation result of the inference engine 103, the modified original expert knowledge can be stored in the knowledge base management system.
Optionally, the inference engine 103 includes a neural network algorithm trained by using the facts and expert knowledge of the user as a training set, and the inference engine 103 calculates the problem description using the trained neural network algorithm to obtain the intermediate result.
Optionally, the inference engine 103 is further configured to revise the expert knowledge in the knowledge base:
marking the original expert knowledge to be modified, storing the original expert knowledge in a knowledge management system after modification, and completely covering and replacing the original expert knowledge through the management of the knowledge management system; alternatively, both coexist.
Optionally, the integrated database is further configured to record raw expert knowledge and facts, and the operation information includes system operation time communication state information and operation state information.
Optionally, the interpreter 105 is also used for tracking, recording, interpreting the reasoning process. Alternatively, the process may be carried out in a single-stage,
the interpreter 105 tracks the running information of the system running, supplements the conclusion drawn by reasoning, adds configuration and description, processes the reasoning process and conclusion into the description which is convenient for understanding, and finally records and sends the result.
Optionally, the human-machine interface 106 is also used for counting and displaying the final result.
Optionally, the human-machine interface 106 is further configured to make manual corrections to the expert knowledge.
Optionally, the user facts include fault information and diagnostic related raw data information.
Optionally, the user facts are sent by each maintenance subsystem APP, network management system, and other facts source devices included in the train control system monitoring maintenance device.
The invention has at least one of the following advantages:
the traditional maintenance terminal only maintains the self equipment, and the method can be commonly used for all maintenance equipment;
the method is an intelligent diagnosis method based on an expert system, and reduces the problems of low efficiency and slow effect of manual diagnosis; for complex alarms, the scheme supports intelligent algorithms such as a neural network and the like, and diagnosis suggestions are more scientifically and efficiently provided for users; the scheme has strong expansibility, and can supplement and expand diagnosis contents in combination with actual scenes.
Drawings
FIG. 1 is a block diagram of a generalized fault diagnosis platform system based on an expert system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a TIS and RBC communication alarm processing service provided by an embodiment of the invention;
FIG. 3 is a table I showing a simple listing of fault diagnosis information examples for a column control interlock integrated device;
FIG. 4 is a list II defining a list for an input alarm information matrix;
fig. 5 is a list III defining a list for the output alarm diagnosis result matrix.
Detailed Description
The generalized fault diagnosis platform system based on the expert system provided by the invention is further described in detail below with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for the purpose of facilitating and clearly aiding in the description of embodiments of the invention. For a better understanding of the invention with objects, features and advantages, refer to the drawings. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that any modifications, changes in the proportions, or adjustments of the sizes of structures, proportions, or otherwise, used in the practice of the invention, are included in the spirit and scope of the invention which is otherwise, without departing from the spirit or essential characteristics thereof.
As shown in fig. 1, the present embodiment provides a generalized fault diagnosis platform system based on an expert system, including: a knowledge acquisition module 101, configured to acquire a user fact from the train control system monitoring maintenance device; in this embodiment, the user facts are sent by the maintenance subsystems APP, the network management system, and other facts source devices 201. The knowledge base and the management system 102 thereof are connected with the knowledge acquisition module 101; wherein the knowledge base is used for storing expert knowledge; the knowledge base management system is used for retrieving, organizing and maintaining the expert knowledge in the knowledge base;
a comprehensive database 104 connected with the knowledge base and its management system 102, for receiving and storing the user facts from the knowledge base and its management system 102, and describing problems according to the user facts; the inference machine 103 is connected with the comprehensive database 104 and is used for processing the received problem description to obtain an intermediate result; an interpreter 105, which is respectively connected with the inference engine 103 and the comprehensive database 104, wherein the interpreter 105 is used for processing the intermediate result according to the received operation information to obtain a final result; and the man-machine interface 106 is connected with the interpreter 105 and is used for outputting the final result to each maintenance subsystem APP202 in the train control system monitoring maintenance equipment.
In the present embodiment, the expert knowledge includes the experiential knowledge of the expert, the principle knowledge within the field, and the related facts.
In this embodiment, the knowledge acquisition module 101 also acquires expert knowledge from the user and transmits the expert knowledge to the knowledge base and its management system 102.
Namely knowledge acquisition: the collection and arrangement work of knowledge is mainly to input expert knowledge and fact data of the field into a knowledge base, organize the expert knowledge and the fact data according to a specific data structure and check consistency and integrity of logic before and after maintaining the knowledge.
In this embodiment, the knowledge base management system is configured to retrieve, organize and maintain the expert knowledge in the knowledge base; specifically, firstly, knowledge is collected, then organized according to a specific (established) data structure, consistency and integrity of logic before and after maintaining expert knowledge are checked, the checked expert knowledge is stored, the checked expert knowledge is participated in the expert knowledge of the inference engine 103, and learning can be performed again, if the original expert knowledge is limited according to the operation result of the inference engine 103, the modified original expert knowledge can be stored in a knowledge base management system (specifically, the original expert knowledge before and after modification is stored in the knowledge base management system together).
Namely, the knowledge base and its management system 102: the knowledge base is used to store the experiential knowledge of the expert, the principle knowledge within the domain, and the related facts. The management system is responsible for retrieving, organizing and maintaining expert knowledge in the library, and when inconsistent with the conclusion of the inference engine 103, modifying the expert knowledge or both coexist.
In this embodiment, the inference engine 103 includes a neural network algorithm trained by using user facts and expert knowledge as a training set, and the inference engine 103 calculates the problem description using the trained neural network algorithm to obtain the intermediate result.
In this embodiment, the inference engine 103 is further configured to revise the expert knowledge in the knowledge base: the specific process marks the original expert knowledge to be modified, modifies the original expert knowledge into more reasonable expert knowledge, stores the more reasonable expert knowledge in a knowledge management system, and manages whether the original expert knowledge is completely replaced or coexisted through the knowledge management system.
That is, the inference engine 103 is a "thinking" mechanism of the expert system, and is configured to simulate the thinking process of the expert in the field to solve the problem, and for the more complex problem, sample learning can be performed by means of an intelligent algorithm, and when the learned expert knowledge proves that the original expert knowledge has a certain limitation, the revised expert knowledge can be fed back to the knowledge base, and the knowledge base stores and manages the revised expert knowledge.
In this embodiment, the comprehensive database records original expert knowledge and facts, and is further configured to store the intermediate result after the inference determination, and the final result and information such as a communication state and a working state when the system operates.
In this embodiment, the interpreter 105 is configured to process the intermediate result according to the received operation information to obtain a final result: specifically, in this embodiment, the interpreter 105 tracks information such as communication and working state during system operation, supplements the conclusion obtained by reasoning, increases configuration and description, makes the reasoning process and conclusion more convenient to understand, and finally records and sends the result.
That is, the interpreter 105 may output diagnostic advice (end results) to the user in a contracted form via a human-machine interface.
In this embodiment, the human interface 106 is also used to count and display the final result.
In this embodiment, the human-machine interface 106 is further configured to manually modify the expert knowledge.
That is, the man-machine interface 106 serves as an interactive interface between a domain expert or a knowledge engineer and a general user for outputting, counting and displaying diagnosis suggestions, and can implement manual correction of expert knowledge and directly affect diagnosis results.
In this embodiment, the user facts include fault information and diagnostic related raw data information.
Referring to fig. 1, a generalized maintenance diagnosis platform system structure based on an expert system is illustrated, and a third technical scheme is detailed, which is not described herein; referring to fig. 2, a neural network-based intelligent diagnostic method is illustrated. Referring to table I, fault information based on a neural network algorithm, and an input layer and an output layer are shown.
Referring first to fig. 2, the design construction method of the present embodiment is described by way of example, and includes the following steps:
step 10, the train control interlocking integrated equipment simply enumerates fault diagnosis information as shown in table I (shown in fig. 3).
Step 11, according to the neural network, an input layer of the neuron is made, that is, user facts output by each maintenance subsystem APP, network management system, and other facts source device 201: TISDM (note: train control interlocking integrated maintenance terminal abbreviation) alarm information { alarm equipment, occurrence time, communication layer, security layer }, TISPS (note: train control interlocking integrated security host abbreviation) and RBC interface information (original message is not actually checked by grabbing packets on a security switch at the later stage at present), TISPS dubug logs, network management port detection alarms (abnormal flow and the like), TISPS board network port indication lamps, switch network port indication lamps and TISPS platform DMRT logs (comprising custom alarm codes, errors can be positioned, but error generation reasons need to be checked in combination with grabbing packets).
Step 12, according to the neural network, making an output layer of the neuron, namely, sending a fault diagnosis conclusion to each maintenance subsystem APP 202: TISPS software problem, TISPS data problem, internet port problem, switch problem, RBC software problem, RBC data problem.
Step 13, carrying out a label definition list on the needed input and output information according to a matrix form, wherein the label definition list is shown in a table II (shown in fig. 4) and a table III (shown in fig. 5) respectively:
in connection with business processes, we make knowledge reasoning as in fig. 2:
step 20, firstly, the alarm and the corresponding associated alarm information are obtained; i.e. the knowledge acquisition module 101 acquires this alarm and the corresponding associated alarm information.
Step 21, they are then logically combined, and the association and combination of alarms are defined by the advance configuration, i.e. the knowledge base and its management system 102 performs this step 21.
Step 22, a logical decision is then made on these combinations, sometimes with other input information and decision conditions in between, until an alarm diagnosis is output, i.e. the inference engine 103 performs this step 22.
And step 23, finally, informing relevant manufacturer personnel to track and position according to the output alarm reasons until the process is finished.
In step 24, the original data of the 5 th packet is obtained immediately when the fault is not generated, and later organization is needed for reproduction. We do not add this content when we make a general speculation. But if any, a secondary algorithm may be supplemented to locate a particular problem with softening or data, i.e., the interpreter 105 performs this step 24.
Step 25, packaging the intermediate algorithm logic into a black box. The problem under the general high probability is found by using the algorithm such as the neural network, and the probability of the alarm cause is output, namely, the inference engine 103 executes the step S5.
Step 26, a large number of samples are used for training a 6×8 matrix to obtain weights, namely, the inference engine 103 executes the step 26;
step 27, if more knowledge is added, such as DMRT log alarm code definition, the developer can be assisted to further locate the software internal problem, i.e. the inference engine 103 performs step 27.
Step 28, a model of integrated health management maintenance diagnostics is generated, extending into the diagnostic application of all network transmission alarm devices of the train control system, i.e. the human interface 106 performs step 28.
The method provides thinking for the intellectualization of the fault diagnosis of the prior train control system equipment, combines the characteristics of train control equipment maintenance diagnosis, designs and explains a system interface of a generalized maintenance diagnosis platform based on an expert system, provides solutions for three problems in the design of the expert system, and analyzes and describes different methods of reasoning under different communication information. Finally, the intelligent maintenance diagnosis method is further explained by train control interlocking integrated fault communication alarm modeling and combining an expert system technology and a neural network technology. The new method integrates the general fault diagnosis of the train control equipment, integrates the station and center information, is convenient for the maintenance of the train control system equipment, and provides possibility for intelligent traffic and large traffic in the future.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the apparatus and methods disclosed in the embodiments herein may be implemented in other ways. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments herein may be integrated together to form a single part, or the modules may exist alone, or two or more modules may be integrated to form a single part.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (13)
1. An expert system-based generalized fault diagnosis platform system, comprising:
a knowledge acquisition module (101) for acquiring user facts from the train control system monitoring maintenance equipment;
a knowledge base and a management system (102) thereof, which are connected with the knowledge acquisition module (101); wherein the knowledge base is used for storing expert knowledge; the knowledge base management system is used for retrieving, organizing and maintaining the expert knowledge in the knowledge base;
-a comprehensive database (104) connected to said knowledge base and its management system (102) for receiving and storing said user facts from said knowledge base and its management system (102), and for performing a problem description based on the user facts;
the inference engine (103) is connected with the comprehensive database (104) and is used for processing the received problem description to obtain an intermediate result;
an interpreter (105) connected to the inference engine (103) and the comprehensive database (104), respectively, the interpreter (105) being configured to process the intermediate result according to the received operation information to obtain a final result;
and the man-machine interface (106) is connected with the interpreter (105) and is used for outputting the final result to each maintenance subsystem APP (202) in the train control system monitoring maintenance equipment.
2. The expert system-based generalized fault diagnosis platform system according to claim 1, wherein said expert knowledge comprises expert empirical knowledge, intra-domain principle knowledge, and related facts.
3. The expert system-based generalized fault diagnosis platform system according to claim 2, wherein said knowledge acquisition module (101) is further configured to acquire expert knowledge and transmit it to said knowledge base and its management system (102).
4. The expert system-based generalized fault diagnosis platform system according to claim 3, wherein said knowledge base management system (102) is configured to retrieve, organize and maintain said expert knowledge within said knowledge base; specifically, expert knowledge is collected firstly, then organized according to a given data structure, consistency and integrity of logic before and after the expert knowledge is checked and maintained, and the checked expert knowledge is stored;
and (3) relearning expert knowledge participated in the inference engine (103), and if the original expert knowledge is limited according to the operation result of the inference engine (103), storing the modified original expert knowledge into the knowledge base management system.
5. The expert system-based generalized fault diagnosis platform system according to claim 4, wherein said inference engine (103) comprises a trained neural network algorithm using user facts and expert knowledge as a training set, said inference engine (103) employing said trained neural network algorithm to compute said problem description to obtain said intermediate result.
6. The expert system-based generalized fault diagnosis platform system according to claim 5, wherein said inference engine (103) is further configured to revise said expert knowledge in said knowledge base:
marking the original expert knowledge to be modified, storing the original expert knowledge in a knowledge management system after modification, and completely covering and replacing the original expert knowledge through the management of the knowledge management system; alternatively, both coexist.
7. The expert system-based generalized fault diagnosis platform system of claim 6, wherein said integrated database is further configured to record raw expert knowledge and facts, and said operational information includes system runtime communication status information and operational status information.
8. The expert system-based generalized fault diagnosis platform system according to claim 7, wherein said interpreter (105) is further configured to track, record, interpret an inference process.
9. The expert system-based generalized fault diagnosis platform system according to claim 8, wherein said interpreter (105) tracks said operational information of the system while running, supplements inferences drawn by reasoning, adds configuration and explanation, processes inferences to an explanation that is easy to understand, and finally records and sends results.
10. The expert system-based generalized fault diagnosis platform system of claim 9, wherein said human-machine interface (106) is further configured to count and display said final result.
11. The expert system-based generalized fault diagnosis platform system according to claim 10, wherein said human-machine interface (106) is further configured to manually modify said expert knowledge.
12. The expert system-based generalized fault diagnosis platform system of claim 11, wherein said user facts comprise fault information and diagnosis-related raw data information.
13. The expert system-based generalized fault diagnosis platform system of claim 12, wherein said user facts are issued by respective maintenance subsystems APP, network management systems, and other facts-source devices included in said train control system monitoring maintenance facility.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116994686A (en) * | 2023-09-26 | 2023-11-03 | 北斗云方(北京)健康科技有限公司 | Data driven clinical decision support system and method |
| CN117081234A (en) * | 2023-06-28 | 2023-11-17 | 国网江苏省电力有限公司淮安供电分公司 | Intelligent substation safety measure checking expert system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117081234A (en) * | 2023-06-28 | 2023-11-17 | 国网江苏省电力有限公司淮安供电分公司 | Intelligent substation safety measure checking expert system |
| CN116994686A (en) * | 2023-09-26 | 2023-11-03 | 北斗云方(北京)健康科技有限公司 | Data driven clinical decision support system and method |
| CN116994686B (en) * | 2023-09-26 | 2023-12-15 | 北斗云方(北京)健康科技有限公司 | Data driven clinical decision support system and method |
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