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Peer-reviewedCC BY 4.0PLOS One

Concave sparsity-assisted time-reassigned multisynchrosqueezing transform for early-stage weak fault feature enhancement with applications to bearing fault diagnosis

  • Delong Zhang1
  • Yubo Ma2
  • Hongan Wu3
  1. 1Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, P.R. China
  2. 2Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang, P.R. China
  3. 3Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
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Abstract

Early-stage defects in wind turbine bearings often generate weak impulsive signatures that are easily obscured by strong background noise, posing a significant challenge to accurate extraction of fault-related features. To overcome this limitation, a novel concave sparsity-assisted time-reassigned multisynchrosqueezing transform (CSA-TMSST) is developed to improve the extraction of weak fault-related signatures. First, a non-convex sparse optimization model incorporating the generalized minimax-concave (GMC) penalty is established to reconstruct fault impulsive components from the raw signal. The introduced GMC-based sparse regularization enhances signal sparsity, enabling the accurate extraction of impulsive fault signatures. Subsequently, TMSST is employed to characterize the reconstructed signal in the time-frequency (TF) domain, yielding a more concentrated time-frequency representation (TFR). Furthermore, the iterative reassignment mechanism is utilized to enhance the concentration of TF energy distribution, thereby yielding a clearer TFR of transient fault features. Finally, characteristic fault frequencies are identified via spectral analysis. The results demonstrate that CSA-TMSST is capable of extracting weak fault-related signatures and producing high-quality TFRs under strong noise conditions, confirming its effectiveness and superiority.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026concave,
  title = {{Concave sparsity-assisted time-reassigned multisynchrosqueezing transform for early-stage weak fault feature enhancement with applications to bearing fault diagnosis}},
  author = {Delong Zhang and Yubo Ma and Hongan Wu},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360733},
  url = {https://doi.org/10.1371/journal.pone.0360733},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Concave sparsity-assisted time-reassigned multisynchrosqueezing transform for early-stage weak fault feature enhancement with applications to bearing fault diagnosis
AU  - Delong Zhang
AU  - Yubo Ma
AU  - Hongan Wu
PY  - 2026
DA  - 2026/10/16
JO  - PLOS One
DO  - 10.1371/journal.pone.0360733
UR  - https://doi.org/10.1371/journal.pone.0360733
AB  - Early-stage defects in wind turbine bearings often generate weak impulsive signatures that are easily obscured by strong background noise, posing a significant challenge to accurate extraction of fault-related features. To overcome this limitation, a novel concave sparsity-assisted time-reassigned multisynchrosqueezing transform (CSA-TMSST) is developed to improve the extraction of weak fault-related signatures. First, a non-convex sparse optimization model incorporating the generalized minimax-concave (GMC) penalty is established to reconstruct fault impulsive components from the raw signal. The introduced GMC-based sparse regularization enhances signal sparsity, enabling the accurate extraction of impulsive fault signatures. Subsequently, TMSST is employed to characterize the reconstructed signal in the time-frequency (TF) domain, yielding a more concentrated time-frequency representation (TFR). Furthermore, the iterative reassignment mechanism is utilized to enhance the concentration of TF energy distribution, thereby yielding a clearer TFR of transient fault features. Finally, characteristic fault frequencies are identified via spectral analysis. The results demonstrate that CSA-TMSST is capable of extracting weak fault-related signatures and producing high-quality TFRs under strong noise conditions, confirming its effectiveness and superiority.
SN  - 1932-6203
ER  -