Advancing Predictive Maintenance: A Study of Domain Adaptation for Fault Identification in Gearbox Components
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Published
Sep 4, 2023
Shinya Tsuruta
Koji Wakimoto
Takaaki Nakamura
Shahin Siahpour
Marcella Miller
John Taco
Jay Lee
Abstract
This paper explores the use of machine learning in predictive maintenance, which has been increasingly demanded in recent years to reduce downtime and maintenance burden. The challenge of different data distributions between training and test data in machine learning is common in predictive maintenance where equipment operation patterns can change, leading to reduced operational efficiency. The authors validate a domain-adaptive anomaly detection method combining CNN and MMD, which achieves similar accuracy with PCA, SVD, and other dimensionality reduction methods. The study also shows that the method maintains accuracy even when the number of normal data in the target domain is 1/10 of the source domain.
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Keywords
predictive maintenance, domain adaptation, machine learning, convolutionnal neural network, maximum mean discrepancy
References
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Siahpour, S., Li, X., & Lee, J. (2022). A novel transfer learning approach in remaining useful life prediction for incomplete dataset. IEEE Transactions on Instrumentation and Measurement, 71, 1-11.
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Vincent, V., Wannes, M., & Jesse, D. (2020). Transfer learning for anomaly detection through localized and unsupervised instance selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(4), 6054-6061.
Yang, L., Ma, Y., Zeng, F., Peng, X., & Liu, D. (2021). Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability. Microelectronics Reliability, 114311.
Zhang, B., Li, W., Tong, Z., & Zhang, M. (2017). Bearing fault diagnosis under varying working condition based on domain adaptation. arXiv preprint, arXiv:1707.09890.
Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X. (2021). Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions. Reliability Engineering & System Safety, 211, 107556.
Zhou, F., Yang, S., Fujita, H., Chen, D., & Wen, C. (2020). Deep learning fault diagnosis method based on global optimization gan for unbalanced data. KnowledgeBased Systems, 187, 104837.
Buijs, R., Koch, T., & Dugundji, E. (2021). Applying transfer learning and various ann architectures to predict transportation mode choice in amsterdam. Procedia Computer Science, 184, 532-540.
Costa, P. R. d. O. d., Akc ̧ay, A., Zhang, Y., & Kaymak, U. (2020). Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195, 106682.
Grezmak, J., Wang, P., Sun, C., & Gao, R. X. (2019). Explainable convolutional neural network for gearbox fault diagnosis. Procedia CIRP, 80, 476-481.
Han, T., Liu, C., Yang, W., & Jiang, D. (2019). A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-based systems, 165, 474-487.
Jiang, G., Zhao, J., Jia, C., He, Q., Xie, P., & Meng, Z. (2017). Intelligent fault diagnosis of gearbox based on vibration and current signals: a multimodal deep learning approach. Prognostics and System Health Management Conference (PHM-Qingdao), 1-6.
Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement, 11, 1-10.
Li, X., Ding, Q., & Sun, J. Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1-11.
Liao, L., & Lee, J. (2009). A novel method for machine performance degradation assessment based on fixed cycle features test. Journal of Sound and Vibration, 326(3-5), 894-908.
Liu, C., Mauricio, A., Chen, Z., Declercq, K., Meerten, Y., Vonderscher, Y., & Gryllias, K. (2020). Gear grinding monitoring based on deep convolutional neural networks. IFAC-PapersOnLine, 53(2), 10324-10329.
Mahyari, A. G., & Locker, T. (2018). Domain adaptation for robot predictive maintenance systems. arXiv preprint, arXiv:1809.08626.
Michau, G., & Fink, O. (2021). Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer. Knowledge-Based Systems, 216, 106816.
Purarjomandlangrudi, A., Ghapanchi, A. H., & Esmalifala. (2014). A data mining approach for fault diagnosis: An application of anomaly detection algorithm. Measurement, 55, 343-352.
Qiao, W., & Lu, D. (2015). A survey on wind turbine condition monitoring and fault diagnosis—part ii: Signals and signal processing methods. IEEE Transactions on Industrial Electronics, 62(10), 6546-6557.
Saufi, S. R., Ahmad, Z. A. B., Leong, M. S., & Lim, M. H. (2019). Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review. Ieee Access, 7, 644-662.
Siahpour, S., Li, X., & Lee, J. (2022). A novel transfer learning approach in remaining useful life prediction for incomplete dataset. IEEE Transactions on Instrumentation and Measurement, 71, 1-11.
Soualhi, A., Hawwari, Y., Medjaher, K., Clerc, G., Hubert, R., & Guillet, F. (2018). Phm survey: implementation of signal processing methods for monitoring bearings and gearboxes. International Journal of Prognostics and Health Management, 9(2), 0.
Vincent, V., Wannes, M., & Jesse, D. (2020). Transfer learning for anomaly detection through localized and unsupervised instance selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(4), 6054-6061.
Yang, L., Ma, Y., Zeng, F., Peng, X., & Liu, D. (2021). Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability. Microelectronics Reliability, 114311.
Zhang, B., Li, W., Tong, Z., & Zhang, M. (2017). Bearing fault diagnosis under varying working condition based on domain adaptation. arXiv preprint, arXiv:1707.09890.
Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X. (2021). Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions. Reliability Engineering & System Safety, 211, 107556.
Zhou, F., Yang, S., Fujita, H., Chen, D., & Wen, C. (2020). Deep learning fault diagnosis method based on global optimization gan for unbalanced data. KnowledgeBased Systems, 187, 104837.
Section
Special Session Papers
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