Rail transit fault diagnosis method and system based on rough set

A fault diagnosis and rail transit technology, applied in rail transit technology and information fields, can solve problems such as high risk, low efficiency, and large workload, and achieve the effect of reducing risk, improving work efficiency, and improving efficiency and accuracy.

Active Publication Date: 2014-04-09
BEIJING TAILEDE INFORMATION TECH
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Problems solved by technology

[0004] In order to solve the technical problems of large workload, low efficiency and high risk in the manual diagnosis of railway signal ...

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  • Rail transit fault diagnosis method and system based on rough set
  • Rail transit fault diagnosis method and system based on rough set
  • Rail transit fault diagnosis method and system based on rough set

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Embodiment Construction

[0039] The present invention will be further described below through specific embodiments and accompanying drawings.

[0040] The invention provides a rough set-based rail traffic monitoring data analysis and fault diagnosis method, which can solve the technical problems of heavy workload, low efficiency, and high risk in the prior art when manually diagnosing faults in railway signaling systems.

[0041]Rough set theory is a new method of discrete data reasoning. At present, rough set method has become one of the main techniques in data mining applications. Its basic idea is to divide the domain of discourse of the problem according to the existing knowledge of the given problem, and then determine the degree of belonging to a decision set for each component after the division. The basic idea of ​​rough set theory to find classification rules in the database is to divide data objects into corresponding subsets according to different attribute values ​​of attributes, and then ...

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Abstract

The invention relates to a rail transit diagnosis method and system based on a rough set. The method comprises the following steps: (1) collecting monitoring data of rail transit signal equipment and extracting the characteristics of the collected monitoring data so as to establish a fault diagnosis decision table, (2) based on the rough set, conducting knowledge extraction and attribute reduction on the fault diagnosis decision table so as to obtain a best attribute reduction combination, (3) establishing a neural network model, using the condition attribute in the best attribute reduction combination as input, using the decision attribute of the best attribute reduction combination as the output target of a neural network, and adopting the neural network for training, (4) using the trained neural network for calculating the possibility of a possible fault area of the real-time fault information, using the fault area largest in possibility as a fault diagnosis result and outputting the result. The rail transit diagnosis method can solve the problems that work load is large, efficiency is low, and risk performance is high when rail signal system failures are judged manually, and improves the efficiency and the accuracy of rail transit data analysis and failure diagnosis.

Description

technical field [0001] The invention belongs to the field of rail transit technology and information technology, and relates to a rough set-based rail transit fault diagnosis method and system. Background technique [0002] At present, there are three main types of monitoring and maintenance products in the field of rail transit (state-owned railways, enterprise railways and urban rail transit): CSM (Centralized Signal Monitoring System), various equipment maintenance machines, and communication network management systems. In order to improve the modern maintenance level of my country's railway signal system equipment, since the 1990s, TJWX-I and TJWX-2000 have been independently developed and continuously upgraded signal centralized monitoring CSM systems. At present, most of the stations have adopted computer monitoring system to realize the real-time monitoring of the signal equipment status of the station, and by monitoring and recording the main operating status of the ...

Claims

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Application Information

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IPC IPC(8): G06N3/02
Inventor 鲍侠
Owner BEIJING TAILEDE INFORMATION TECH
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