Train control onboard device fault classification and recognition method based on rough set-neural network model

Abstract

The invention provides a train control onboard device fault classification and recognition method based on a rough set-neural network model. The method comprises steps: according to a fault case library analyzed and sorted by a train control onboard device fault log file, a corresponding relationship between a fault class and a fault code is dug out, fault codes and fault classes in the fault caselibrary are coded, an initial decision table is generated, and a classification rule is determined; RST is used to carry out attribute reduction on the initial decision table, and a final decision rule is generated; and based on the final decision rule, a neural network model is built, and the neural network model is used to realize fault recognition on the train control onboard device. Accordingto the fault classification and recognition method with the neural network and the rough set theory combined provided in the invention, the problems of low fault recognition rate for text fault dataof a high noise-containing train control onboard device, poor incomplete knowledge processing ability and the like can be solved, and accuracy of the fault classification and recognition for the traincontrol onboard device can be ensured.

Description

technical field [0001] The invention relates to the technical field of fault identification of train control vehicle equipment, in particular to a fault classification and identification method for train control vehicle equipment based on a rough set-neural network model. Background technique [0002] Fault diagnosis technology has always been a major focus and difficulty in the field of engineering applications. Therefore, a lot of research work has been carried out and significant results have been achieved. [0003] With the continuous deepening of research, fault diagnosis technology has gradually developed into a relatively mature subject, but due to the characteristics of train operation control system, there are not many fault diagnosis methods that are really suitable for train control system, mainly including fault tree and expert system. , Bayesian network, etc., but they are all based on the system level, and they do not have an advantage in dealing with the fault...

AI Technical Summary

Problems solved by technology

[0003] With the continuous deepening of research, fault diagnosis technology has gradually developed into a relatively mature subject, but due to the characteristics of train operation control system, there are not many fault diagnosis methods that are really suitable for train control system, mainly including fault tree and expert system. , Bayesian network, etc., but they are all based on the system level, and do not have an advantage in dealing with the faults of train control vehicle equipment based on text data, and have the disadvantages of low fault resolution and poor processing ability for uncertain knowledge

[0004] At present, the fault diagnosis and positioning of train control on-board equipment in the prior art mainly rely on the manual diagnosis of a large number of maintenance personnel, which is time-consuming and laborious, and the diagnosis accuracy is low

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