Journal of Mechanical Science and Technology

, Volume 31, Issue 6, pp 2711–2722 | Cite as

A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM

  • Yongjian LiEmail author
  • Weihua Zhang
  • Qing Xiong
  • Dabing Luo
  • Guiming Mei
  • Tao Zhang


A novel rolling bearing fault diagnosis strategy is proposed based on Improved multiscale permutation entropy (IMPE), Laplacian score (LS) and Least squares support vector machine-Quantum behaved particle swarm optimization (QPSO-LSSVM). Entropy-based concepts have attracted attention recently within the domain of physiological signals and vibration data collected from human body or rotating machines. IMPE, which was developed to reduce the variability of entropy estimation in time series, was used to obtain more precise and reliable values in rolling element bearing vibration signals. The extracted features were then refined by LS approach to form a new feature vector containing main unique information. By constructing the fault feature, the effective characteristic vector was input to QPSO-LSSVM classifier to distinguish the health status of rolling bearings. The comparative test results indicate that the proposed methodology led to significant improvements in bearing defect identification.


Multiscale permutation entropy Laplacian score Feature extraction Least squares support vector machines Fault diagnosis 


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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yongjian Li
    • 1
    Email author
  • Weihua Zhang
    • 1
  • Qing Xiong
    • 2
  • Dabing Luo
    • 3
  • Guiming Mei
    • 1
  • Tao Zhang
    • 1
  1. 1.State Key Laboratory of Traction PowerSouthwest Jiaotong UniversityChengduChina
  2. 2.School of Automobile & TransportationXihua UniversityChengduChina
  3. 3.School of Mechanical EngineeringSouthwest Jiaotong UniversityChengduChina

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