摘要
文章针对高速列车行走部故障识别难的问题,提出了一种基于谐波小波包分解和相像系数的故障特征提取与分类识别方法,用谐波小波包分解法对各类故障信号进行多层分解,从中提取能反映各类故障特征的频带能量信息组成特征向量,通过计算和比较信号特征向量与不同故障特征向量的相像系数,实现对故障的识别与分类。实验结果表明,该方法能有效地识别列车正常、空气弹簧失气、抗蛇形减震器全拆及横向减震器全拆4种情况,同时在不同速度下均取得了满意的识别率,验证了该方法的有效性。
To solve the problem that the fault identification of high-speed train is difficult, a fault feature extraction and identification method based on harmonic wavelet packet decomposition and resemblance coefficient is proposed. Harmonic wavelet packet transform is used to extract fault feature vector consisting of the band energy which can reflect the characteristics of various types of fault. By calculating and comparing the resemblance coefficient of the signal feature vector and different types of fault feature vectors, the method can identify and classify the faults. Experimental results show that the situations including the normal train, spring air of loss air, without anti-yaw shock absorber and without transverse shock absorber can be identified effectively. At different speeds, the identification rates are satisfactory and the validity of the method is verified.
出处
《合肥工业大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第6期650-654,共5页
Journal of Hefei University of Technology：Natural Science
基金
国家自然科学基金资助项目(61134002)
关键词
高速列车
谐波小波
特征提取
相像系数
故障识别
high-speed train
harmonic wavelet
feature extraction
resemblance coefficient
fault i- dentification
