Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

被引：223

作者：

Wu, Shuen-De ^{[2
]}

Wu, Po-Hung ^{[1
]}

Wu, Chiu-Wen ^{[2
]}

Ding, Jian-Jiun ^{[1
]}

Wang, Chun-Chieh ^{[3
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan

[2] Natl Taiwan Univ, Dept Mechatron Technol, Taipei 10610, Taiwan

[3] Ind Technol Res Inst, Mech & Syst Res Labs, Hsinchu 31040, Taiwan

来源：

ENTROPY | 2012年 / 14卷 / 08期

关键词：

fault diagnosis; machine vibration; multiscale; permutation entropy; multiscale permutation entropy; support vector machine; APPROXIMATE ENTROPY; COMPLEXITY; TOOL;

D O I：

10.3390/e14081343

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).

引用

页码：1343 / 1356

页数：14

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