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Frequency adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors
  • +3
  • Aosheng Tian,
  • Ye Zhang,
  • Chao Ma,
  • Huiling Chen,
  • Shilin Zhou,
  • Weidong Sheng
Aosheng Tian
National University of Defense Technology College of Electronic Science and Technology

Corresponding Author:tianaosheng20@nudt.edu.cn

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Ye Zhang
National University of Defense Technology College of Electronic Science and Technology
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Chao Ma
National University of Defense Technology College of Electronic Science and Technology
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Huiling Chen
National University of Defense Technology College of Electronic Science and Technology
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Shilin Zhou
National University of Defense Technology College of Electronic Science and Technology
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Weidong Sheng
National University of Defense Technology College of Electronic Science and Technology
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Abstract

The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi-sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self-attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine-grained manner. The experimental results on two multi-sensor-based fault diagnosis datasets demonstrate the anti-noise capability of our proposed method.
05 Sep 2022Submitted to Electronics Letters
06 Sep 2022Submission Checks Completed
06 Sep 2022Assigned to Editor
22 Sep 2022Reviewer(s) Assigned
08 Oct 2022Review(s) Completed, Editorial Evaluation Pending
14 Oct 2022Editorial Decision: Revise Minor
28 Oct 20221st Revision Received
30 Oct 2022Submission Checks Completed
30 Oct 2022Assigned to Editor
30 Oct 2022Review(s) Completed, Editorial Evaluation Pending
30 Oct 2022Editorial Decision: Accept