China Mechanical Engineering

Previous Articles     Next Articles

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

WANG Zhenya;YAO Ligang   

  1. School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou,350116
  • Online:2020-10-25 Published:2020-10-29



  1. 福州大学机械工程及自动化学院,福州,350116
  • 基金资助:

Abstract: 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.

Key words: generalized refined composite multiscale sample entropy(GRCMSE), discriminant diffusion maps analysis(DDMA), fault diagnosis, manifold learning, rolling bearing

摘要: 针对滚动轴承故障特征提取困难的问题,提出了一种广义精细复合多尺度样本熵(GRCMSE)与流形学习相结合的特征提取方法。利用GRCMSE提取滚动轴承故障特征信息;采用判别式扩散映射分析(DDMA)方法对高维特征进行降维处理;将低维故障特征输入粒子群优化支持向量机多故障分类器中进行故障识别。滚动轴承故障实验分析结果表明:GRCMSE特征提取效果优于多尺度样本熵(MSE)、精细复合多尺度样本熵(RCMSE)和广义多尺度样本熵(GMSE); DDMA降维效果优于等度规映射(Isomap)和局部切空间排列(LTSA)的降维效果;GRCMSE和DDMA相结合后的滚动轴承故障识别精度达到100%。

关键词: 广义精细复合多尺度样本熵, 判别式扩散映射分析, 故障诊断, 流形学习, 滚动轴承

CLC Number: