摘要
为在线诊断运行列车的轴承状态,提出一种基于核特征矩阵联合近似对角化(kernel joint approximate diagonalization of eigen-matrices,简称KJADE)的列车轴承轨边声学故障诊断方法。首先,从校正后的轨边信号中提取原始特征,将其通过非线性映射函数映射到高维特征空间;其次,对特征空间的核矩阵进行四阶累积量的特征分解,获得新融合特征,并采用支持向量机分类器对融合特征进行辨识;最后,对轴承外圈、内圈、滚子故障和正常4种状态下的列车轨边声学信号进行分析。结果表明,该方法可以有效实现对列车轴承轨边声音信号的非线性特征提取,提高了故障的识别率。
A novel method of wayside acoustic fault diagnosis for locomotive bearings is presented to evaluate the bearings status of running train,which mapped the original features extracted from corrected acoustic signals into a high-dimensional feature space through nonlinear mapping.Where,the original features are extracted from bearing′s acoustic signal acquired by the wayside microphone after Doppler distortion correction being conducted.In the high-dimension feature space,new features can be fused through the eigen-decomposition of the fourth-order cumulative kernel matrix.And the fusion features are extracted from acoustic signals of locomotive bearings with normal condition,inner race defect,outer race defect and ball defect respectively,and identified using the support vector machine classifier.The results show that the proposed method is efficient in nonlinear feature extraction from bearing′s acoustic signals and can obtain high accuracy in fault identification.
作者
龙磊
何兵
刘方
刘永斌
李桂华
陆思良
LONG Lei;HE Bing;LIU Fang;LIU Yongbin;LI Guihua;LU Siliang(College of Electrical Engineering and Automation,Anhui University Hefei,230601,China;National Engineering Laboratory of Energy-Saving Motor&Control Technology Hefei,230601,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2020年第4期781-787,828,829,共9页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51675001,51505001,51605002)。
关键词
核特征矩阵联合近似对角化
列车轴承
故障诊断
多普勒校正
kernel joint approximate diagonalization of eigen-matrices(KJADE)
locomotive bearing
fault diagnosis
Doppler effect correction
