Detection of faults in rotating machinery using periodic time-frequency sparsity

Yin Ding, Wangpeng He, Binqiang Chen, Yanyang Zi, Ivan W. Selesnick

Research output: Contribution to journalArticlepeer-review

Abstract

This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature manifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization–minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization–minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show that the proposed approach can effectively detect and extract the periodical oscillatory features.

Original languageEnglish (US)
Pages (from-to)357-378
Number of pages22
JournalJournal of Sound and Vibration
Volume382
DOIs
StatePublished - Nov 10 2016

Keywords

  • Fault diagnosis
  • Group sparsity denoising
  • Non-convex optimization
  • Rotating machinery

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Acoustics and Ultrasonics
  • Mechanical Engineering

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