Online inverse solution for deep learning-based prognostics
Tianzhi Li, Morteza Moradi, Ming Xiao, Lihui Wang
Abstract. Data-driven prognostic models have been extensively utilized in current structural health monitoring (SHM) practices. They are designed to provide the health indicator (HI) – a representation of the system’s current health state – through sensor data. To enhance performance, online learning is often used to take care of uncertainties that arise from the run-to-failure process. The inverse solution, though demonstrated in online uncertainty quantification applications, remains unexplored in the context of online data-driven prognostics. Therefore, this work proposes a generic inverse solution for a deep prognostic model to online address uncertainties. The proposed method is tested using the open-access XJTU-SY bearing datasets, showcasing its capacity to online enhance the performance of a given model.
Keywords
Structural Health Monitoring, Remaining Useful Life Prediction, Inverse Solution, State and Parameter Estimation, Multiple Local Particle Filter
Published online 3/25/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Tianzhi Li, Morteza Moradi, Ming Xiao, Lihui Wang, Online inverse solution for deep learning-based prognostics, Materials Research Proceedings, Vol. 50, pp 119-126, 2025
DOI: https://doi.org/10.21741/9781644903513-14
The article was published as article 14 of the book Structural Health Monitoring
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] T. Li, Particle filter-based fatigue damage prognosis using prognostic-aided model updating, Mechanical Systems and Signal Processing, 211 (2024) 111244. https://doi.org/10.1016/j.ymssp.2024.111244
[2] L. Lomazzi, R. Junges, M. Giglio, F. Cadini, Unsupervised data-driven method for damage localization using guided waves, Mechanical Systems and Signal Processing, 208 (2024) 111038. https://doi.org/10.1016/j.ymssp.2023.111038
[3] J. Tian, D. Han, H.R. Karimi, Y. Zhang, P. Shi, A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data, Neural Networks, (2024) 106167. https://doi.org/10.1016/j.neunet.2024.106167
[4] D. Yang, H.R. Karimi, L. Gelman, An explainable intelligence fault diagnosis framework for rotating machinery, Neurocomputing, 541 (2023) 126257. https://doi.org/10.1016/j.neucom.2023.126257
[5] R. Zhong, Y. Feng, P. Li, X. Wu, A. Guo, A. Zhang, C. Li, Uncertainty-aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm, IET Collaborative Intelligent Manufacturing, 6 (2024) e12108. https://doi.org/10.1049/cim2.12108
[6] C. Lai, P. Baraldi, E. Zio, Physics-Informed deep Autoencoder for fault detection in New-Design systems, Mechanical Systems and Signal Processing, 215 (2024) 111420. https://doi.org/10.1016/j.ymssp.2024.111420
[7] J. Tian, D. Han, M. Li, P. Shi, A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis, Knowledge-Based Systems, 243 (2022) 108466. https://doi.org/10.1016/j.knosys.2022.108466
[8] R. Junges, Z. Rastin, L. Lomazzi, M. Giglio, F. Cadini, Convolutional autoencoders and CGANs for unsupervised structural damage localization, Mechanical Systems and Signal Processing, 220 (2024) 111645. https://doi.org/10.1016/j.ymssp.2024.111645
[9] X. Zhou, C. Sbarufatti, M. Giglio, L. Dong, A fuzzy-set-based joint distribution adaptation method for regression and its application to online damage quantification for structural digital twin, Mechanical Systems and Signal Processing, 191 (2023) 110164. https://doi.org/10.1016/j.ymssp.2023.110164
[10] J. Chen, S. Yuan, C. Sbarufatti, X. Jin, Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring, Reliability Engineering & System Safety, 215 (2021) 107758. https://doi.org/10.1016/j.ress.2021.107758
[11] T. Li, L. Lomazzi, F. Cadini, C. Sbarufatti, J. Chen, S. Yuan, Numerical simulation-aided particle filter-based damage prognosis using Lamb waves, Mechanical Systems and Signal Processing, 178 (2022) 109326. https://doi.org/10.1016/j.ymssp.2022.109326
[12] T. Li, J. Chen, S. Yuan, F. Cadini, C. Sbarufatti, Particle filter-based damage prognosis using online feature fusion and selection, Mechanical Systems and Signal Processing, 203 (2023) 110713. https://doi.org/10.1016/j.ymssp.2023.110713
[13] X. Zhou, S. He, L. Dong, S.N. Atluri, Real-Time Prediction of Probabilistic Crack Growth with a Helicopter Component Digital Twin, AIAA Journal, 60 (2022) 2555-2567. https://doi.org/10.2514/1.J060890
[14] M. Chiachío, J. Chiachío, S. Sankararaman, K. Goebel, J. Andrews, A new algorithm for prognostics using Subset Simulation, Reliability Engineering & System Safety, 168 (2017) 189-199. https://doi.org/10.1016/j.ress.2017.05.042
[15] D. Cristiani, C. Sbarufatti, M. Giglio, Damage diagnosis and prognosis in composite double cantilever beam coupons by particle filtering and surrogate modelling, Structural Health Monitoring, (2020) 1475921720960067. https://doi.org/10.12783/shm2019/32281
[16] T. Li, C. Sbarufatti, F. Cadini, Multiple local particle filter for high-dimensional system identification, Mechanical Systems and Signal Processing, 209 (2024) 111060. https://doi.org/10.1016/j.ymssp.2023.111060
[17] T. Li, F. Cadini, M. Chiachío, J. Chiachío, C. Sbarufatti, Particle filter-based delamination shape prediction in composites subjected to fatigue loading, Structural Health Monitoring, 22 (2023) 1844-1862. https://doi.org/10.1177/14759217221116041
[18] P. Kundu, A.K. Darpe, M.S. Kulkarni, A review on diagnostic and prognostic approaches for gears, Structural Health Monitoring, 20 (2020) 2853-2893. https://doi.org/10.1177/1475921720972926
[19] B. Wang, Y. Lei, N. Li, N. Li, A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings, IEEE Transactions on Reliability, 69 (2020) 401-412. https://doi.org/10.1109/TR.2018.2882682
[20] B. Wang, Y. Lei, T. Yan, N. Li, L. Guo, Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery, Neurocomputing, 379 (2020) 117-129. https://doi.org/10.1016/j.neucom.2019.10.064
[21] G. Galanopoulos, N. Eleftheroglou, D. Milanoski, A. Broer, D. Zarouchas, T. Loutas, A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures, Composite Structures, 306 (2023) 116579. https://doi.org/10.1016/j.compstruct.2022.116579
[22] M. Moradi, F.C. Gul, D. Zarouchas, A novel machine learning model to design historical-independent health indicators for composite structures, Composites Part B: Engineering, 275 (2024) 111328. https://doi.org/10.1016/j.compositesb.2024.111328
[23] M. Moradi, A. Broer, J. Chiachío, R. Benedictus, T.H. Loutas, D. Zarouchas, Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data, Engineering Applications of Artificial Intelligence, 117 (2023) 105502. https://doi.org/10.1016/j.engappai.2022.105502
[24] X. Liu, Y. Lei, N. Li, X. Si, X. Li, RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting, Mechanical Systems and Signal Processing, 185 (2023) 109788. https://doi.org/10.1016/j.ymssp.2022.109788
[25] Q. Ni, J.C. Ji, K. Feng, Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network, IEEE Transactions on Industrial Informatics, 19 (2023) 1301-1311. https://doi.org/10.1109/TII.2022.3169465
[26] R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, M. Helu, Cloud-enabled prognosis for manufacturing, CIRP Annals, 64 (2015) 749-772. https://doi.org/10.1016/j.cirp.2015.05.011
[27] F. Zeng, Y. Li, Y. Jiang, G. Song, An online transfer learning-based remaining useful life prediction method of ball bearings, Measurement, 176 (2021) 109201. https://doi.org/10.1016/j.measurement.2021.109201
[28] J. Zhuang, Y. Cao, M. Jia, X. Zhao, Q. Peng, Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions, Expert Systems with Applications, 227 (2023) 120276. https://doi.org/10.1016/j.eswa.2023.120276
[29] W. Mao, K. Liu, Y. Zhang, X. Liang, Z. Wang, Self-Supervised Deep Tensor Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction Across Machines, IEEE Transactions on Instrumentation and Measurement, 72 (2023) 1-16. https://doi.org/10.1109/TIM.2023.3265109
[30] C. Liu, L. Zhang, Y. Zheng, Z. Jiang, J. Zheng, C. Wu, Online industrial fault prognosis in dynamic environments via task-free continual learning, Neurocomputing, 598 (2024) 127930. https://doi.org/10.1016/j.neucom.2024.127930
