Abstract
Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm
Highlights
ZPW-2000K non-insulated track circuit is based on the introduction and localization of French UM71 track circuit technology, combined with China's national conditions, and proposed a system with high security, high transmission and high reliability
This paper proposes a fault diagnosis method based on Rough Sets (RS) reduction model and Bayesian Network (BN) structure learning fusion
A diagnostic decision table is established through the fault instance, and RS is used for attribute reduction, dimensionality reduction, and simplified model
Summary
ZPW-2000K non-insulated track circuit is based on the introduction and localization of French UM71 track circuit technology, combined with China's national conditions, and proposed a system with high security, high transmission and high reliability. It is important equipment for China's high-speed railway signal system, and it is a key equipment to ensure the smooth and safe operation of railway high-speed trains and efficient transportation. In [3], based on the working principle and fault characteristics of the track circuit, the FNN fault diagnosis model is established, but the neural network is easy to fall into local optimum. The BN model was verified and analyzed by the fault instance of the ZPW-2000K track circuit of a high-speed railway station
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