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中文摘要：
针对列车走行部的多种故障模式,提出了基于排列组合熵的高速列车走行部故障诊断方法。对原车和抗蛇行减振器失效、空气弹簧失气、横向减振器失效四种工况进行仿真实验,得到列车不同位置的振动信号。计算列车振动信号的排列组合熵,首先实现了同一速度下不同故障的分离,然后以排列组合熵作为故障特征向量,对特征向量进行多级SVM分类识别。实验结果表明,该方法可以有效识别列车同一速度下的不同故障,高速时对四种工况的平均识别率达97%以上。
英文摘要：
In view of a variety of system failure modes of train bogie,this paper proposed a fault diagnosis method based on permutation entropy. There were four typical working conditions in simulation experiment,such as normal condition and yaw damper fault,air spring fault,lateral damper fault. Calculating the permutation entropy of train,vibration signal,firstly,it achieved the separation of different faults with the same speed,then it regarded the permutation entropy as the fault feature,used multistage SVM to classify the feature vector. The experiment results show that the method has a good performance on classifying different faults with the same speed of train,the average recognition rate of four types is above 97% when the speed is high.
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