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Journal of Mechanical Science and Technology

, Volume 33, Issue 1, pp 109–120 | Cite as

Refined composite multiscale fuzzy entropy: Localized defect detection of rolling element bearing

  • Yongjian Li
  • Bingrong MiaoEmail author
  • Weihua Zhang
  • Peng Chen
  • Jihua Liu
  • Xiaoliang Jiang
Article
  • 2 Downloads

Abstract

We proposed an appealing method based on refined composite multiscale fuzzy entropy (RCMFE), infinite feature selection (Inf-FS) algorithm, and support vector machine (SVM) for implementing localized defect detection to keep the downtime and extended damage caused by incipient failure of bearing at a minimum. As a useful approach, multiscale fuzzy entropy (MFE) was utilized to measure the complexity and dynamic changes of signals. However, an inaccurate entropy value would be yielded with the increase of scale factor. Here, as an improvement version of MFE, the RCMFE was proposed to address the shortcomings in the case of short time series. For this novel method, we conducted a full investigation of the effects and robustness by comparing the proposed method with two other entropy-based approaches using synthetic signals and real data. Results indicate that the proposed algorithm outperforms the other approaches in terms of reliability and stability. The RCMFE values of bearing signals from one healthy condition and seven fault states are calculated as diagnostic information. Moreover, an intelligent fault identification method was constructed by combining the Inf-FS algorithm and SVM classifier. Experimental results show the usefulness of the proposed strategy.

Keywords

Defect detection Infinite feature selection Multiscale fuzzy entropy Refined composite technique Rolling bearing condition 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yongjian Li
    • 1
    • 2
    • 3
  • Bingrong Miao
    • 2
    Email author
  • Weihua Zhang
    • 2
  • Peng Chen
    • 4
  • Jihua Liu
    • 1
  • Xiaoliang Jiang
    • 5
  1. 1.School of Railway Tracks and TransportationWuyi UniversityJiangmenChina
  2. 2.State Key Laboratory of Traction PowerSouthwest Jiaotong UniversityChengduChina
  3. 3.Key Laboratory of Automotive Measurement, Control and SafetyXihua UniversityChengduChina
  4. 4.School of Mechanical EngineeringSouthwest Jiaotong UniversityChengduChina
  5. 5.College of Mechanical EngineeringQuzhou UniversityQuzhouChina

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