Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Khandaker Noman, Yongbo Li, Guangrui Wen, Anayet U. Patwari, Shun Wang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.

Original languageEnglish
Pages (from-to)40-56
Number of pages17
JournalStructural Health Monitoring
Volume23
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • continuous health monitoring
  • degradation assessment
  • early fault detection
  • fuzzy entropy
  • Rolling element bearing

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