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A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Author

Listed:
  • Abdenour Soualhi

    (Laboratory LASPI EA-3059, University of Jean Monnet, 42100 Saint Etienne, France)

  • Bilal El Yousfi

    (Laboratory LASPI EA-3059, University of Jean Monnet, 42100 Saint Etienne, France)

  • Hubert Razik

    (Laboratory Ampère UMR 5005, University of Lyon, 69007 Lyon, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

Suggested Citation

  • Abdenour Soualhi & Bilal El Yousfi & Hubert Razik & Tianzhen Wang, 2021. "A Novel Feature Extraction Method for the Condition Monitoring of Bearings," Energies, MDPI, vol. 14(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2322-:d:539661
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    References listed on IDEAS

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    1. Jiejunyi Liang & Jian-Hua Zhong & Zhi-Xin Yang, 2017. "Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery," Energies, MDPI, vol. 10(10), pages 1-14, October.
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    Cited by:

    1. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    2. Shu Han & Xiaoming Liu & Yan Yang & Hailin Cao & Yuanhong Zhong & Chuanlian Luo, 2021. "Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals," Energies, MDPI, vol. 14(22), pages 1-13, November.

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