Rolling Bearing Fault Diagnosis Method Based on Generalized Refined Composite Multiscale Sample Entropy and Manifold Learning

被引：0

作者：

Wang Z. ^{[1
]}

Yao L. ^{[1
]}

机构：

[1] School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2020年 / 31卷 / 20期

关键词：

Discriminant diffusion maps analysis(DDMA); Fault diagnosis; Generalized refined composite multiscale sample entropy(GRCMSE); Manifold learning; Rolling bearing;

D O I：

10.3969/j.issn.1004-132X.2020.20.009

中图分类号：

学科分类号：

摘要：

Aiming at the difficulty of extracting fault features of rolling bearings, a feature extraction method was proposed based on GRCMSE and manifold learning. GRCMSE was utilized to extract the features of rolling bearings.DDMA method was employed to reduce the dimension of the high-dimensional feature sets. The low-dimensional fault features were input into particle swarm optimization support vector machine(PSO-SVM) multi-fault classifier for fault identification. The experimental results of rolling bearing fault diagnosis show that the features extraction effectiveness of GRCMSE is better than that of MSE, RCMSE and GMSE, the dimensionality reduction effectiveness of DDMA is preferable to Isomap and local tangent space alignment(LTSA), the fault recognition accuracy of rolling bearings reaches 100% by combining GRCMSE and DDMA. © 2020, China Mechanical Engineering Magazine Office. All right reserved.

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页码：2463 / 2471

页数：8

共 14 条

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