IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v253y2025ics0951832024006288.html
   My bibliography  Save this article

A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis

Author

Listed:
  • Keshun, You
  • Puzhou, Wang
  • Peng, Huang
  • Yingkui, Gu

Abstract

Although current deep learning models for bearing fault diagnosis have achieved excellent accuracy, the lack of constraint-guided learning of the physical mechanisms of real bearing failures and a physically scientific training paradigm leads to low interpretability and unreliability of intelligent fault diagnosis models. In this study, a sound-vibration physical-information fusion constraint-guided (PFCG) deep learning (DL) method is proposed, aiming at weighted fusion of sound and vibration multi-physical information into a deep learning model, to guide the DL model to learn more realistic physical laws of bearing failure. Firstly, a 15-degree-of-freedom nonlinear dynamics model of multi-stage degraded bearing failure mechanism with sound-vibration response is developed, which considers the evolutionary mechanism of bearing failure from healthy state to different stages, and utilizes a particle filtering algorithm for dynamic calibration of hidden parameters. Moreover, a lightweight DL fault diagnosis model is designed to realize the deep interaction between the physical model and the DL model through the weighted fusion of the cross-entropy loss function, physical consistency loss and uncertainty loss. Moreover, the superior diagnostic performance of the proposed sound and vibration PFCG-DL model is verified by comparing the performance fluctuations and parameter attributes of different DL benchmark models before and after being guided by physical information fusion constraints (PFCG). Eventually, the proposed PFCG-Transformer model achieves a diagnostic accuracy of 99.45% while keeping the number of parameters at only 0.62M, which significantly improves the accuracy and reduces the computational complexity by 81.5% compared to the CAME-Transformer model's 3.24 M number of parameters and 95.00% diagnostic accuracy. In addition, the test time of PFCG-Transformer is reduced to 1.02 s, which is 60.2% less than CAME-Transformer, demonstrating higher computational efficiency and real-time performance. Importantly, in terms of interpretability, the engineering interpretability and credibility of the models are further improved by visualizing the feature learning results of the vibration modal and multimodal fusion models and the sensitivity analyses of the sound-vibration response models with internal and external physical hyperparameters. Therefore, this study proposes a physical information-guided deep learning method with strong interpretability and superior performance, which provides an important reference for further research and application in the field of bearing fault diagnosis.

Suggested Citation

  • Keshun, You & Puzhou, Wang & Peng, Huang & Yingkui, Gu, 2025. "A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006288
    DOI: 10.1016/j.ress.2024.110556
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024006288
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110556?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. 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).
    2. Wang, Haoyu & Li, Chuanjiang & Ding, Peng & Li, Shaobo & Li, Tandong & Liu, Chenyu & Zhang, Xiangjie & Hong, Zejian, 2024. "A novel transformer-based few-shot learning method for intelligent fault diagnosis with noisy labels under varying working conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Jiang, Yu & Zhu, Hua & Li, Z., 2016. "A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 8-19.
    6. 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).
    7. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Wang, Haoyu & Li, Chuanjiang & Ding, Peng & Li, Shaobo & Li, Tandong & Liu, Chenyu & Zhang, Xiangjie & Hong, Zejian, 2024. "A novel transformer-based few-shot learning method for intelligent fault diagnosis with noisy labels under varying working conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Zhang, Jianping & Zhang, Yinjie & Fu, Jian & Zhao, Dawen & Liu, Ping & Zhang, Zhiwei, 2024. "Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. Yu, Tian & Li, Chaoshun & Huang, Jie & Xiao, Xiangqu & Zhang, Xiaoyuan & Li, Yuhong & Fu, Bitao, 2024. "ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. Wu, Jiawei & Wan, Liangqi, 2024. "Reliability sensitivity analysis for RBSMC: A high-efficiency multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. 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).
    8. Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    9. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Zhang, Zhongwei & Jiao, Zonghao & Li, Youjia & Shao, Mingyu & Dai, Xiangjun, 2024. "Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    11. Liu, Ruonan & Xie, Yunfei & Lin, Di & Zhang, Weidong & Ding, Steven X., 2024. "Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    12. Zheng, Rui & Najafi, Seyedvahid & Zhang, Yingzhi, 2022. "A recursive method for the health assessment of systems using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    14. Zheng, Yu & Chen, Liang & Bao, Xiangyu & Zhao, Fei & Zhong, Jingshu & Wang, Chenhan, 2025. "Prediction model optimization of gas turbine remaining useful life based on transfer learning and simultaneous distillation pruning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    15. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    16. Santos, Augusto César de Jesus & Cavalcante, Cristiano Alexandre Virgínio & Wu, Shaomin, 2023. "Maintenance policies and models: A bibliometric and literature review of strategies for reuse and remanufacturing," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    17. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    18. Lian, Zheng & Zhou, Zhi-Jie & Hu, Chang-Hua & Wang, Jie & Zhang, Chun-Chao & Zhang, Chao-Li, 2024. "A health assessment method with attribute importance modeling for complex systems using belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    19. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    20. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    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:253:y:2025:i:c:s0951832024006288. 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.