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Author:

Zhao, Dezun (Zhao, Dezun.) | Shao, Depei (Shao, Depei.) | Cui, Lingli (Cui, Lingli.)

Indexed by:

EI Scopus

Abstract:

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults. © 2024 ISA

Keyword:

Image thinning Epicyclic gears Network coding Image coding Wind turbines Image reconstruction Image segmentation Image compression

Author Community:

  • [ 1 ] [Zhao, Dezun]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Shao, Depei]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Cui, Lingli]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China

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Source :

ISA Transactions

ISSN: 0019-0578

Year: 2024

Volume: 154

Page: 335-351

7 . 3 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 61

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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