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首页> 外文会议>Proceedings of 2016 IEEE Information Technology,Networking, Electronic and Automation Control Conference >Fault diagnosis method for railway turnout control circuit based on information fusion
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Fault diagnosis method for railway turnout control circuit based on information fusion

机译：基于信息融合的铁路道岔控制电路故障诊断方法

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摘要

High speed railway turnout is an important signal equipment that is directly contacted with the high speed train. However, it is still in a simple way to deal with the faults of the control circuit by simple instruments and artificial experience. In order to realize the intelligence of the fault diagnosis method for the turnout control circuit, this paper summarizes 11 typical fault modes and 8 corresponding typical fault features. Then, according to the fuzzy theory and neural network, the fault diagnosis is respectively realized by multi factor fuzzy evaluation and three layer BP neural network model. But both two methods cannot solve this problem well. They still can't satisfactorily deal with the problem of false positives and false negatives, which threatens the safety of railway operations. Therefore, based on the Dempster-Shafer evidence theory, this paper further proposes a comprehensive evaluation method of fault diagnosis on the information fusion decision-making level, and achieves the complementary fusion of the two methods, and also increases the accuracy of fault diagnosis. From the verification of the simulation experiments, the method is more accurate than any of the two simple methods of fault diagnosis, and it is promising to have a good application prospect in this field.
机译：高速铁路道岔是直接与高速列车接触的重要信号设备。但是，通过简单的仪器和人工经验来处理控制电路的故障仍然是简单的方法。为了实现智能化的道岔控制电路故障诊断方法，本文总结了11种典型故障模式和8种相应的典型故障特征。然后，根据模糊理论和神经网络，分别通过多因素模糊评价和三层BP神经网络模型实现故障诊断。但是两种方法都不能很好地解决这个问题。他们仍然不能令人满意地处理误报和误报问题，这些问题威胁到铁路运营的安全。因此，基于Dempster-Shafer证据理论，本文进一步提出了一种基于信息融合决策水平的故障诊断综合评价方法，实现了两种方法的互补融合，提高了故障诊断的准确性。通过仿真实验的验证，该方法比两种简单的故障诊断方法更准确，在该领域具有广阔的应用前景。

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