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A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings

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  • Zuo, Tao
  • Zhang, Kai
  • Zheng, Qing
  • Li, Xianxin
  • Li, Zhixuan
  • Ding, Guofu
  • Zhao, Minghang

Abstract

Wavelet transform, a time-frequency analysis method for evaluating non-stationary signals, can assist in representing equipment degradation over prolonged usage. However, a single wavelet basis function is challenging to apply to all periodic transient waveforms. As a result, this research suggests a hybrid attention-based multi-wavelet coefficient fusion method for evaluating the remaining useful life (RUL) of bearings. Firstly, a two-dimensional map is created by organizing the decomposed individual frequency bands after the approach employs several wavelets to get the original signal properties. Secondly, a hybrid attention-based ConvLSTM (HA-ConvLSTM) network is designed to weight wavelet coefficient channels adaptively. The learned features are used to evaluate RULs by a multi-layer perceptron. Finally, tests were run on the PHM2012 rolling bearing dataset to validate the proposed method. Overall, the suggested scheme outperforms previous comparable methods in the performance index. This approach optionally resolves the wavelet basis function matching issue for periodic transient waveforms.

Suggested Citation

  • Zuo, Tao & Zhang, Kai & Zheng, Qing & Li, Xianxin & Li, Zhixuan & Ding, Guofu & Zhao, Minghang, 2023. "A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s095183202300251x
    DOI: 10.1016/j.ress.2023.109337
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    References listed on IDEAS

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    1. Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
    2. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    4. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Jian Ma & Hua Su & Wan-lin Zhao & Bin Liu, 2018. "Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning," Complexity, Hindawi, vol. 2018, pages 1-13, July.
    6. Shiza Mushtaq & M. M. Manjurul Islam & Muhammad Sohaib, 2021. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review," Energies, MDPI, vol. 14(16), pages 1-24, August.
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    Cited by:

    1. Xiang, Sheng & Li, Penghua & Huang, Yi & Luo, Jun & Qin, Yi, 2024. "Single gated RNN with differential weighted information storage mechanism and its application to machine RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Liang, Pengfei & Tian, Jiaye & Wang, Suiyan & Yuan, Xiaoming, 2024. "Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Yang, Shilong & Tang, Baoping & Wang, Weiying & Yang, Qichao & Hu, Cheng, 2024. "Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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