Fusing physics-based and deep learning models for prognostics

Citations

Cited by:

1. Varbella, Anna & Gjorgiev, Blazhe & Sansavini, Giovanni, 2023. "Geometric deep learning for online prediction of cascading failures in power grids," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
2. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
3. Giannakeas, Ilias N. & Mazaheri, Fatemeh & Bacarreza, Omar & Khodaei, Zahra Sharif & Aliabadi, Ferri M.H., 2023. "Probabilistic residual strength assessment of smart composite aircraft panels using guided waves," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
4. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
5. Gjorgiev, Blazhe & Das, Laya & Merkel, Seline & Rohrer, Martina & Auger, Etienne & Sansavini, Giovanni, 2023. "Simulation-driven deep learning for locating faulty insulators in a power line," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
6. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
7. Wang, Yilin & Li, Yuanxiang & Zhang, Yuxuan & Lei, Jia & Yu, Yifei & Zhang, Tongtong & Yang, Yongshen & Zhao, Honghua, 2024. "Incorporating prior knowledge into self-supervised representation learning for long PHM signal," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
8. Xiao, Dasheng & Lin, Zhifu & Yu, Aiyang & Tang, Ke & Xiao, Hong, 2024. "Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
9. Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
10. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
11. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
12. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
13. Zhou, Liang & Wang, Huawei & Xu, Shanshan, 2023. "Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
14. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
15. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
16. Wang, Yilin & Shen, Lei & Zhang, Yuxuan & Li, Yuanxiang & Zhang, Ruixin & Yang, Yongshen, 2023. "Self-supervised Health Representation Decomposition based on contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
17. Nejjar, Ismail & Geissmann, Fabian & Zhao, Mengjie & Taal, Cees & Fink, Olga, 2024. "Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
18. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
19. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
20. Hedrick, Katherine & Omell, Benjamin & Zitney, Stephen E. & Bhattacharyya, Debangsu, 2024. "Development of a health monitoring framework: Application to a supercritical pulverized coal-fired boiler," Energy, Elsevier, vol. 290(C).
21. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
22. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
23. Zhou, Zhihao & Zhang, Wei & Yao, Peng & Long, Zhenhua & Bai, Mingling & Liu, Jinfu & Yu, Daren, 2024. "More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
24. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
25. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
26. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
27. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
28. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
29. Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).