CycleGAN-based Data Augmentation for Enhanced Remaining Useful Life Prediction Under Unsupervised Domain Adaptation
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Abstract
Predictive maintenance is crucial for enhancing operational efficiency and reducing costs in Prognostics and Health Management (PHM). One of the key tasks in predictive maintenance is the estimation of Remaining Useful Life (RUL) of machinery. In practice, the data for different machines is not always accessible in sufficient quantity or quality, therefore the machine learning models trained on machines in one domain often perform poorly when applied to other domains due to covariate shifts. As a solution, Domain Adaptation (DA) aims to tackle domain shifts by extracting domain-invariant features. However, traditional methods often fail to adequately address the complexity and variability of real-world data. We propose to address this challenge, using a Wasserstein CycleGAN with Gradient Penalty (W-CycleGAN-GP) to learn mappings between domains and generate augmented data in the target domain from data in the source domain. We use our approach to generate realistic augmented data that bridge domain gap coupled with recent work on adversarial-based and correlation alignment-based DA models to improve the performance of RUL prediction models in target domains without having access to labeled data. The experimental results on the C-MAPSS dataset demonstrate a significant improvement in the RUL prediction score and accuracy within the target domain.
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Machine Learning, Remaining Useful Life, Predictive Maintenance, Deep Learning
2. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in neural information processing systems, 29.
3. da Costa, P. R. d. O., Akc.ay, A., Zhang, Y., & Kaymak, U. (2020). Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195, 106682.
4. Donahue, C., McAuley, J., & Puckette, M. (2018). Adversarial audio synthesis. arXiv preprint arXiv:1802.04208.
5. Engel, J., Agrawal, K. K., Chen, S., Gulrajani, I., Donahue, C., & Roberts, A. (2019). Gansynth: Adversarial neural audio synthesis. arXiv preprint arXiv:1902.08710.
6. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., . . . Lempitsky, V. (2016). Domainadversarial training of neural networks. Journal of machine learning research, 17(59), 1–35.
7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2020, oct). Generative adversarial networks. Commun. ACM, 63(11), 139–144. Retrieved from https://doi.org/10.1145/3422622 doi: 10.1145/3422622
8. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of wasserstein gans. , 5769–5779.
9. Hsu, W.-N., Zhang, Y., & Glass, J. (2017). Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation. , 16–23.
10. Hu, T., Guo, Y., Gu, L., Zhou, Y., Zhang, Z., & Zhou, Z. (2022). Remaining useful life estimation of bearings under different working conditions via wasserstein distance-based weighted domain adaptation. Reliability Engineering & System Safety, 224, 108526.
11. Iacono, P., & Khan, N. (2023). Structure preserving cyclegan for unsupervised medical image domain adaptation. arXiv preprint arXiv:2304.09164.
12. Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 4401–4410).
13. Li, X., Li, J., Zuo, L., Zhu, L., & Shen, H. T. (2022). Domain adaptive remaining useful life prediction with transformer. IEEE Transactions on Instrumentation and Measurement, 71, 1-13. doi: 10.1109/TIM.2022.3200667
14. Long, M., Cao, Z., Wang, J., & Jordan, M. I. (2018). Conditional adversarial domain adaptation. Advances in neural information processing systems, 31.
15. Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2013). Transfer feature learning with joint distribution adaptation. , 2200–2207.
16. Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2014). Transfer joint matching for unsupervised domain adaptation. , 1410–1417.
17. Luleci, F., Catbas, F. N., & Avci, O. (2023). Cyclegan for undamaged-to-damaged domain translation for structural health monitoring and damage detection. Mechanical Systems and Signal Processing, 197, 110370.
18. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
19. Nejjar, I., Geissmann, F., Zhao, M., Taal, C., & Fink, O. (2024). Domain adaptation via alignment of operation profile for remaining useful lifetime prediction. Reliability Engineering & System Safety, 242, 109718.
20. Ozdagli, A., & Koutsoukos, X. (2020). Domain adaptation for structural health monitoring. , 12(1), 9–9.
21. Palladino, J. A., Slezak, D. F., & Ferrante, E. (2020). Unsupervised domain adaptation via cyclegan for white matter hyperintensity segmentation in multicenter mr images. , 11583, 1158302.
22. Pu, Z., Cabrera, D., Li, C., & de Oliveira, J. V. (2023). Sliced wasserstein cycle consistency generative adversarial networks for fault data augmentation of an industrial robot. Expert Systems with Applications, 222, 119754.
23. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434.
24. Saravanan, S. S., Luo, T., & Van Ngo, M. (2023). Tsi-gan: Unsupervised time series anomaly detection using convolutional cycle-consistent generative adversarial networks. , 39–54.
25. Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA ames prognostics data repository, 18, 878–887.
26. Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto- failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
27. Schockaert, C., & Hoyez, H. (2020). Mts-cyclegan: An adversarial-based deep mapping learning network for multivariate time series domain adaptation applied to the ironmaking industry. arXiv preprint arXiv:2007.07518.
28. Sun, B., Feng, J.,&Saenko, K. (2017). Correlation alignment for unsupervised domain adaptation. Domain adaptation in computer vision applications, 153–171.
29. Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. , 443–450.
30. Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017).Adversarial discriminative domain adaptation. , 7167– 7176.
31. Volpi, R., Morerio, P., Savarese, S., & Murino, V. (2018). Adversarial feature augmentation for unsupervised domain adaptation. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 5495– 5504).
32. Wang, Q., Meng, F., & Breckon, T. P. (2023). Data augmentation with norm-ae and selective pseudo-labelling for unsupervised domain adaptation. Neural Networks, 161, 614–625.
33. Zotov, E., & Kadirkamanathan, V. (2021). Cyclestyleganbased knowledge transfer for a machining digital twin. Frontiers in Artificial Intelligence, 4. doi: 10.3389/frai.2021.767451
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