Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets
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Abstract
This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variations. The methodology is demonstrated through a case study on fault diagnosis of a fleet of hydraulic rock drills, which presents challenges due to variations in sensor data between different fault classes and individual machines. Results show significant improvements in classification accuracy, both in validation and testing, upon employing ensemble models and applying domain adaptation. While the study is limited to one case study, it lays the groundwork for exploring the applicability of the proposed methodology in other contexts.
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Domain Adaptation, Fault Diagnosis, Ensemble Learning, Fleet of Assets
Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
Chen, Z., Gryllias, K., & Li, W. (2019). Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mechanical systems and signal processing, 133, 106272.
Duan, L., Xie, M., Wang, J., & Bai, T. (2018). Deep learning enabled intelligent fault diagnosis: Overview and applications. Journal of Intelligent & Fuzzy Systems, 35(5), 5771-5784.
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. The Journal of Machine Learning Research, 13(1), 723-773.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46, 389-422.
He, J., Li, X., Chen, Y., Chen, D., Guo, J., & Zhou, Y. (2021). Deep transfer learning method based on 1d-cnn for bearing fault diagnosis. Shock and Vibration, 2021.
Jakobsson, E., Frisk, E., Krysander, M., & Pettersson, R. (2022). Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data. arXiv preprint arXiv:2203.16121.
Ma, S., & Chu, F. (2019). Ensemble deep learning-based fault diagnosis of rotor bearing systems. Computers in industry, 105, 143-152.
PHM Society. 2022 PHM Conference Data Challenge - PHM Society Data Repository. (2022, July 30). https://data.phmsociety.org/2022-phm-conference-data- challenge
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