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Bearing fault diagnosis base on multi-scale CNN and LSTM model

Author

Listed:
  • Xiaohan Chen

    (Beijing University of Chemical Technology)

  • Beike Zhang

    (Beijing University of Chemical Technology)

  • Dong Gao

    (Beijing University of Chemical Technology)

Abstract

Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system is significantly rely on the extracted features. However, extracting signal characteristic is fairly time consuming and depends on specialized signal processing knowledge. Although some studies have developed highly accurate algorithms, the diagnostic results rely heavily on large data sets and unreliable human analysis. This study proposes an automatic feature learning neural network that utilizes raw vibration signals as inputs, and uses two convolutional neural networks with different kernel sizes to automatically extract different frequency signal characteristics from raw data. Then long short-term memory was used to identify the fault type according to learned features. The data is down-sampled before inputting into the network, greatly reducing the number of parameters. The experiment shows that the proposed method can not only achieve 98.46% average accuracy, exceeding some state-of-the-art intelligent algorithms based on prior knowledge and having better performance in noisy environments.

Suggested Citation

  • Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01600-2
    DOI: 10.1007/s10845-020-01600-2
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    References listed on IDEAS

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    1. Chenxi Wu & Tefang Chen & Rong Jiang & Liwei Ning & Zheng Jiang, 2017. "A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1847-1858, December.
    2. Adrián Rodríguez Ramos & José M. Bernal de Lázaro & Alberto Prieto-Moreno & Antônio José Silva Neto & Orestes Llanes-Santiago, 2019. "An approach to robust fault diagnosis in mechanical systems using computational intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1601-1615, April.
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2018. "Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 937-951, April.
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    Citations

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    Cited by:

    1. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Cuixia Jiang & Hao Chen & Qifa Xu & Xiangxiang Wang, 2023. "Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1667-1681, April.
    3. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    4. Chuanxia Jian & Yinhui Ao, 2023. "Imbalanced fault diagnosis based on semi-supervised ensemble learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3143-3158, October.
    5. Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
    6. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
    8. Li, Xin & Li, Shuhua & Wei, Dong & Si, Lei & Yu, Kun & Yan, Ke, 2024. "Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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