A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree
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- 作者：Yongbo Li liyongbo0532@126.com" class="auth_mail" title="E-mail the corresponding author ; Minqiang Xu ; xumqhit@126.com" class="auth_mail" title="E-mail the corresponding author ; Yu Wei weiyuhrbeu@126.com" class="auth_mail" title="E-mail the corresponding author ; Wenhu Huang leobo28@foxmail.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Local mean decomposition (LMD) ; Multi-scale permutation entropy (MPE) ; Laplacian score (LS) ; Improved support vector machine based binary tree (ISVM-BT) ; Fault diagnosis
- 刊名：Measurement
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：77
- 期：Complete
- 页码：80-94
- 全文大小：2147 K
文摘
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Rolling bearing feature extraction from noise-contaminated sensor signals based on LMD and MPE.
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Design of a new hierarchical structures in the SVM-BT, which leads to the significant performance enhancement.
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Real-time pattern classification based on LS and ISVM-BT.