Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.
How to Cite
##plugins.themes.bootstrap3.article.details##
Self-supervised learning, Bearing fault diagnosis, Predictive maintenance, Vibration analysis
Domingues, R., Cordioli, J., & Braga, D. (2023). Evaluation of fault detection methodology in roller bearings using time-synchronous average. In Proceedings of 27th abcm international congress of mechanical engineering.
Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022). Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine, 39(3), 42–62.
Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., & Tao, D. (2023). A survey on self-supervised learning: Algorithms, applications, and future trends.
Lei, Y. (2016). Intelligent fault diagnosis and remaining useful life prediction of rotating machinery. Butterworth- Heinemann.
Li, G., Wu, J., Deng, C., Wei, M., & Xu, X. (2022). Selfsupervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data. Applied Acoustics, 191, 108663.
Long, J., Chen, Y., Yang, Z., Huang, Y., & Li, C. (2023). A novel self-training semi-supervised deep learning approach for machinery fault diagnosis. International Journal of Production Research, 61(23), 8238–8251.
Morningstar, W., Bijamov, A., Duvarney, C., Friedman, L., Kalibhat, N., Liu, L., . . . others (2024). Augmentations vs algorithms: What works in self-supervised learning. arXiv preprint arXiv:2403.05726.
Mushtaq, S., Islam, M. M., & Sohaib, M. (2021). Deep learning aided data-driven fault diagnosis of rotatory machine: A comprehensive review. Energies, 14(16), 5150.
Noroozi, M., Vinjimoor, A., Favaro, P., & Pirsiavash, H. (2018). Boosting self-supervised learning via knowledge transfer. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 9359– 9367).
Wan, W., Chen, J., Zhou, Z., & Shi, Z. (2022). Self-supervised simple siamese framework for fault diagnosis of rotating machinery with unlabeled samples. IEEE Transactions on Neural Networks and Learning Systems.
Wang, H., Liu, Z., Ge, Y., & Peng, D. (2022). Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data. Knowledge-Based Systems, 239, 107978.
Wang, Z., Liu, T., Wu, X., & Wang, Y. (2024). A fault diagnosis method based on ntfes-fcct for variable working condition bearing signals. IEEE Sensors Journal.
Wang, Z., Yang, J., & Guo, Y. (2022). Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures. Mechanical systems and signal processing, 172, 108964.
Yan, Z., & Liu, H. (2022). Smoco: A powerful and efficient method based on self-supervised learning for fault diagnosis of aero-engine bearing under limited data. Mathematics, 10(15), 2796.
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics— a comprehensive review. IEEE Access, 8, 29857- 29881. doi: 10.1109/ACCESS.2020.2972859
Zhang, W., Chen, D., & Kong, Y. (2021). Self-supervised joint learning fault diagnosis method based on three channel vibration images. Sensors, 21(14), 4774.
Zhang, X., Zhao, B., & Lin, Y. (2021). Machine learning based bearing fault diagnosis using the case western reserve university data: A review. Ieee Access, 9, 155598–155608.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.