%0 Journal Article
%T 基于熵特征的高速列车故障诊断方法<br>Characteristic Analysis of High Speed Train Vibration Based on Entropy Feature
%A 朱明
%A 吴思东
%A 付克昌
%J 振动.测试与诊断
%D 2015
%X 高速列车运行状态正常与否对列车系统的安全性和舒适度有重要影响，为分析高速列车运行状态，根据高速列车振动加速度信号的特点，提出了分割能量熵和奇异熵的故障诊断方法。首先，分析列车振动信号随速度变化的特点，对不同速度下的信号进行不同频率范围的分析；其次，对分析范围内信号分割成N个区间，计算分割能量熵和奇异熵，将分割能量熵特征和奇异熵组成特征向量；最后，利用支持向量机进行故障分类识别。实验数据仿真分析结果表明，车体中、后部横向加速度信号特征对四种典型工况在不同速度下分类识别率均较高，达到95%以上，说明该方法能有效识别出高速列车故障状态。<br>The reliability of the train system is impacted by the running state of high-speed trains. To analyze the running state, and aimed at the vibration acceleration signal of high-speed trains, a characteristic analysis method of segmentation-energy entropy and singular entropy is proposed. Firstly, the characteristics of the vibration acceleration signal changed with velocity are analyzed. Then, the signal is divided into N range. Segmentation-energy entropy and singular entropy are extracted to make feature vectors. Finally, the support vector machine method is used to classify and identify faults. Experimental results show that four kinds of typical working conditions can be accurately identified in lateral acceleration features of the car body in different speeds, that the identification ratio is above 95%, and that the fault state of high speed trains can be identified effectively.
%K 高速列车
%K 振动加速度信号
%K 分割能量熵
%K 奇异熵
%K 支持向量机&lt
%K br&gt
%K high-speed train
%K vibration acceleration signal
%K segmentation-energy entropy
%K singular entropy
%K support vector machine(SVM)
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201502029&flag=1