Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis
Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis
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基于Hankel矩阵的SVD研究及其在滚动轴承故障诊断中的应用
DOI：
10.1016/j.ymssp.2014.07.019
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发表时间：
2015-02-01
影响因子：
8.4
通讯作者：
Chen, Gang
中科院分区：
文献类型：
--
作者：
Jiang, Huiming;Chen, Jin;Chen, Gang
在传统奇异值分解理论的基础上，将奇异值和相邻奇异值比引入到振动信号的特征提取中。所提出的特征提取方法被称为SV-NSVR。结合所选的SV-NSVR特征，采用连续隐马尔可夫模型（CHMM）实现故障自动分类，并将SV-NSVR和CHMM方法应用于滚动轴承故障诊断和性能评估中。仿真和实验结果表明，与其他奇异值分解特征相比，该方法对轴承故障诊断具有更高的准确性，对滚动轴承的性能评估是有效的。(C)2014爱思唯尔有限公司版权所有。
Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification, Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings. (C) 2014 Elsevier Ltd. All rights reserved.