Repetitive transients extraction algorithm for detecting bearing faults

Wangpeng He, Yin Ding, Yanyang Zi, Ivan Selesnick

Research output: Contribution to journalArticle

Abstract

Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.

Original languageEnglish (US)
Pages (from-to)227-244
Number of pages18
JournalMechanical Systems and Signal Processing
Volume84
DOIs
StatePublished - Feb 1 2017

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Bearings (structural)
Failure analysis
Locomotives
Noise abatement
Feature extraction
Decomposition
Defects

Keywords

  • Bearing Fault diagnosis
  • Compound fault diagnosis
  • Convex optimization
  • Cyclostationary signals
  • Feature extraction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Repetitive transients extraction algorithm for detecting bearing faults. / He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan.

In: Mechanical Systems and Signal Processing, Vol. 84, 01.02.2017, p. 227-244.

Research output: Contribution to journalArticle

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