Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction

Mao Gui Niu, Hong Kai Jiang, Ren He Yao

Research output: Contribution to journalArticlepeer-review

Abstract

Currently, accurately extracting early-stage bearing incipient fault features is urgent and challenging. This paper introduces a novel method called adaptive multiscale wavelet-guided periodic sparse representation (AMWPSR) to address this issue. For the first time, the dual-tree complex wavelet transform is applied to construct the linear transformation for the AMWPSR model. This transform offers superior shift invariance and minimizes spectrum aliasing. By integrating this linear transformation with the generalized minimax concave penalty term, a new sparse representation model is developed to recover faulty impulse components from heavily disturbed vibration signals. During each iteration of the AMWPSR process, the impulse periods of sparse signals are adaptively estimated, and the periodicity of the latest sparse signal is augmented using the final estimated period. Simulation studies demonstrate that AMWPSR can effectively estimate periodic impulses even in noisy environments, demonstrating greater accuracy and robustness in recovering faulty impulse components than existing techniques. Further validation through research on two sets of bearing life cycle data shows that AMWPSR delivers superior fault diagnosis results.

Original languageEnglish
Pages (from-to)3585-3596
Number of pages12
JournalScience China Technological Sciences
Volume67
Issue number11
DOIs
StatePublished - Nov 2024

Keywords

  • dual-tree complex wavelet transform
  • generalized minimax concave penalty
  • incipient fault feature extraction
  • periodic sparse representation

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