A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states
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DOI: 10.1016/j.ress.2022.108677
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Cited by:
- Song, Xueying & Qi, Lei & Liu, Shiyan & Ding, Shuiting & Li, Daqing, 2024. "Simple analysis of complex system safety based on Finite State Machine Network and phase space theory," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
- Asgari, Ali & Si, Wujun & Yuan, Liang & Krishnan, Krishna & Wei, Wei, 2024. "Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
- Subramanian, Abhinav & Mahadevan, Sankaran, 2023. "Probabilistic physics-informed machine learning for dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
- Xiaosheng, Si & Li, Huiqin & Zhang, Zhengxin & Li, Naipeng, 2024. "A Wiener-process-inspired semi-stochastic filtering approach for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
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Keywords
Prognostics; Reliability; Physics-informed statistical learning; Archimedean copula; Cubic B-splines; Dynamic models;All these keywords.
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