IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v225y2022ics0951832022002095.html
   My bibliography  Save this article

A multi-layer spiking neural network-based approach to bearing fault diagnosis

Author

Listed:
  • Zuo, Lin
  • Xu, Fengjie
  • Zhang, Changhua
  • Xiahou, Tangfan
  • Liu, Yu

Abstract

Effective fault diagnosis is a crucial way to reduce the occurrence of severe damages of many industrial products. With the increasing amount of condition monitoring data, deep-learning-based methods have become promising ways for intelligent fault diagnosis thanks to their automatic feature extraction capability. Most recently, the third-generation neural network, called spiking neural network (SNN), has been introduced as an effective tool for fault diagnosis. However, the internal state and the error function of neurons in the SNN model cannot satisfy the conditions of continuity and differentiability, resulting in the difficulty of the gradient back-propagation, and it, therefore, prevents the extension of the SNN to a deep manner. In this article, a probabilistic spiking response model (PSRM) with a multi-layer structure is put forth to enhance the performance of the SNN in terms of bearing fault diagnosis. In the PSRM, the extracted features from the local mean decomposition (LMD) method are converted into the probability pulse sequences, and a multi-layer learning algorithm is developed to facilitate the multi-layer network training. The fault diagnosis results from three bearing databases, i.e., CWRU, MFPT, and Paderborn University datasets, demonstrate that the proposed PSRM exceeds a majority of the state-of-the-art machine learning methods. The proposed multi-layer SNN can also provide transparency to different bearing fault patterns by the membrane potentials of the spiking neurons in the output layer.

Suggested Citation

  • Zuo, Lin & Xu, Fengjie & Zhang, Changhua & Xiahou, Tangfan & Liu, Yu, 2022. "A multi-layer spiking neural network-based approach to bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002095
    DOI: 10.1016/j.ress.2022.108561
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022002095
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108561?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
    6. Jianhong Liang & Liping Wang & Jun Wu & Zhigui Liu, 2020. "Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Liu, Jianing & Cao, Hongrui & Luo, Yang, 2023. "An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Liu, Zhao-Hua & Chen, Liang & Wei, Hua-Liang & Wu, Fa-Ming & Chen, Lei & Chen, Ya-Nan, 2023. "A Tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Yu, Xiaolei & Zhao, Zhibin & Zhang, Xingwu & Chen, Xuefeng & Cai, Jianbing, 2023. "Statistical identification guided open-set domain adaptation in fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Liu, Jie & Xu, Huoyao & Peng, Xiangyu & Wang, Junlang & He, Chaoming, 2023. "Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. 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).
    8. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.
    9. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Li, Yan-Fu & Wang, Huan & Sun, Muxia, 2024. "ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Yin, Zhenqin & Zhuo, Yue & Ge, Zhiqiang, 2023. "Transfer adversarial attacks across industrial intelligent systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. Wang, Huan & Li, Yan-Fu, 2023. "Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xin & Yang, Yu & Wu, Zhantao & Yan, Ke & Shao, Haidong & Cheng, Junsheng, 2022. "High-accuracy gearbox health state recognition based on graph sparse random vector functional link network," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    2. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    5. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. 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).
    10. Li, Yan-Fu & Wang, Huan & Sun, Muxia, 2024. "ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    13. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Li, Xin & Zhong, Xiang & Shao, Haidong & Han, Te & Shen, Changqing, 2021. "Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    16. Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    18. 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).
    19. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Mohammed Majid Abdulrazzaq & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts," Mathematics, MDPI, vol. 12(5), pages 1-42, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002095. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.