A fault identification method and system based on neural network self-learning

A neural network and fault identification technology, applied in the direction of biological neural network models, can solve problems such as heavy workload, low efficiency, and high risk, and achieve the effect of accelerating speed, saving labor costs, and speeding up fault identification

Active Publication Date: 2017-03-29
BEIJING TAILEDE INFORMATION TECH
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Problems solved by technology

[0005] In order to solve the technical problems of heavy workload, low efficiency and high risk in manual diagnosis of railway signal system faults in the prior art, the present invention provides a fault identification method and system based on neural network self-learning

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  • A fault identification method and system based on neural network self-learning
  • A fault identification method and system based on neural network self-learning
  • A fault identification method and system based on neural network self-learning

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Embodiment Construction

[0037] The present invention will be described in detail below through specific embodiments and accompanying drawings.

[0038] A fault identification method and system based on neural network self-learning in this embodiment is composed of the following parts: CSM-based data acquisition subsystem, data preprocessing subsystem, feature selection subsystem, model training subsystem, real-time data analysis subsystem system and self-learning subsystem. It is used to solve technical problems such as large workload, low efficiency and high risk in manual diagnosis of railway signal system faults in the prior art.

[0039] The neural network is mainly composed of neurons, and the structure of neurons is as follows: figure 2 As shown, a1~an are the components of the input vector

[0040]w1~wn are the weights of each synapse of neurons

[0041] b is bias

[0042] f is a transfer function, usually a nonlinear function. Generally, there are sigmod(), traind(), tansig(), hardlim()...

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Abstract

A fault recognition method and system based on neural network self-learning. The method comprises: 1) monitoring and collecting various set monitoring quantities of track traffic equipment, and converting the collected monitoring data into sample data applicable to neural network training; 2) classifying the sample data according to the types of faults, and obtaining a sample data set corresponding to each type of fault; 3) designing one neural network for each type of fault, then using a sample data set of the fault for training, and obtaining a recognition model of the type of fault; and 4) fusing recognition models of all the types of faults into one neutral network, and carrying out fault recognition on the monitoring data collected in real time. The method can calmly cope with complex equipment faults and train operation accidents.

Description

technical field [0001] The invention provides a fault identification method and system based on neural network self-learning, which relates to technical fields such as railway signal data, railway communication data, railway knowledge data, system alarm data, machine learning, neural network, self-learning, expert system, etc. In order to solve the problems faced by the data analysis of rail transit monitoring data. Background technique [0002] 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 signal equipment, it provides a basis for the electric department to grasp the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02
CPCG06N3/02
Inventor 鲍侠
Owner BEIJING TAILEDE INFORMATION TECH
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