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A Novel Variable Convolution Kernel Design According to Time-frequency Resolution Altering in Bearing Fault Diagnosis

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Abstract

Timely diagnosis of bearing faults can effectively predict initial faults and avoid severe accidents. Ordinary neural networks have achieved relatively high accuracy for bearing failure classification. However, for the convolution process in neural networks, convolution kernels of fixed size are used across the whole image acquired by the time-frequency transformation, which is prone to overlook the intrinsic local time-frequency features of data. So as to solve this problem that the kernel may not reflect the local time-frequency characteristics of the non-stationary signal, a new method for planning convolutional kernels is proposed in this paper. We leverage variable kernels to capture the time-frequency resolution altering nature within the non-stationary signals. Firstly, starting from the time-frequency characteristics of non-stationary signals, the theoretical basis of adopting variable convolution kernels is analyzed, and the impact of Heisenberg’s measurement inaccuracy principle on the design of the learning framework is analyzed in-depth with wavelet analysis as an example. Secondly, since the performance of different wavelet basis functions on time-frequency resolution varies dramatically, after comprehensive study of the mutual relationship between these resolutions, a proper criteria is deduced to deliver the measurement of performances, and the Gabor wavelet basis is chosen according to this principle. This design can be obtained by using wavelet analysis parameters before learning and training. This method is orthogonal to the model training process and can be plug-and-play with other deep learning frameworks. Finally, it is shown through datasets and practical experiments that the proposed method outperforms other newly proposed classification methods in terms of achieving higher accuracy in less time.

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Data Availability

The data that support the findings of this study are available from the corresponding author, Yang Wang, upon reasonable request.

Code Availability

Due to the nature of this research, participants of this study did not agree for their code to be shared publicly, so supporting code is not available.

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Funding

This work was partially supported by “the innovation foundation of the city of Bengbu, Grant No. JZ2022YDZJ0019” and “the Fundamental and Research Funds for the Central Universities of China, Grant No.PA2021GDSK0095 ”.

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Authors and Affiliations

  1. Institute of Industry & Equipment Technology, Hefei University of Technology, Hefei, 230009, China

    Xu Ding & Hua Zhai

  2. Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment, Hefei, 230009, China

    Xu Ding

  3. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Yang Wang, Hang Zheng & Juan Xu

Authors
  1. Xu Ding
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  2. Yang Wang
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  3. Hang Zheng
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  4. Juan Xu
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  5. Hua Zhai
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Contributions

Conceptualization:Xu Ding and Yang Wang; Methodology: Xu Ding and Yang Wang; Formal analysis:Yang Wang and Hang Zheng; Investigation: Yang Wang; Supervision: Hua Zhai; Writing–original draft: Yang Wang; Writing–review: Xu Ding, Juan Xu, Hua Zhai.

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Correspondence to Hua Zhai.

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Ding, X., Wang, Y., Zheng, H. et al. A Novel Variable Convolution Kernel Design According to Time-frequency Resolution Altering in Bearing Fault Diagnosis. Mobile Netw Appl 28, 406–420 (2023). https://doi.org/10.1007/s11036-023-02094-x

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