A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Abdenour Soualhi, Bilal El Yousfi, Hubert Razik and Tianzhen Wang
Additional contact information
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
Tianzhen Wang: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

Energies, 2021, vol. 14, issue 8, 1-23

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.

Keywords: signal processing; empirical mode decomposition; Hilbert transform; bearing fault; vibration analysis; feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/8/2322/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/8/2322/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:8:p:2322-:d:539661

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().