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Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

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  • Xu, Yanwen
  • Kohtz, Sara
  • Boakye, Jessica
  • Gardoni, Paolo
  • Wang, Pingfeng
Abstract
The computerized simulations of physical and socio-economic systems have proliferated in the past decade, at the same time, the capability to develop high-fidelity system predictive models is of growing importance for a multitude of reliability and system safety applications. Traditionally, methodologies for predictive modeling generally fall into two different categories, namely physics-based approaches and machine learning-based approaches. There is a growing consensus that the modeling of complex engineering systems requires novel hybrid methodologies that effectively integrate physics-based modeling with machine learning approaches, referred to as physics-informed machine learning (PIML). Developing advanced PIML techniques is recognized as an important emerging area of research, which could be particularly beneficial in addressing reliability and system safety challenges. With this motivation, this paper provides a review of the state-of-the-art of physics-informed machine learning methods in reliability and system safety applications. The paper highlights different efforts towards aggregating physical information and data-driven models as grouped according to their similarity and application area within each group. The goal is to provide a collection of research articles presenting recent developments of this emergent topic, and shed light on the challenges and future directions which we, as a research community, should focus on for harnessing the full potential of advanced PIML techniques for reliability and safety applications.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005154
    DOI: 10.1016/j.ress.2022.108900
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    as
    1. Guo, Jian & (Steven) Li, Zhaojun & (Judy) Jin, Jionghua, 2018. "System reliability assessment with multilevel information using the Bayesian melding method," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 146-158.
    2. Li, Rui & Verhagen, Wim J.C. & Curran, Richard, 2020. "A systematic methodology for Prognostic and Health Management system architecture definition," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Patrick A. K. Reinbold & Logan M. Kageorge & Michael F. Schatz & Roman O. Grigoriev, 2021. "Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Zang, Yu & Shangguan, Wei & Cai, Baigen & Wang, Huasheng & Pecht, Michael. G., 2021. "Hybrid remaining useful life prediction method. A case study on railway D-cables," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    5. Chao Hu & Byeng D. Youn & Pingfeng Wang, 2019. "Engineering Design under Uncertainty and Health Prognostics," Springer Series in Reliability Engineering, Springer, number 978-3-319-92574-5, February.
    6. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Hu, Zhen & Mahadevan, Sankaran, 2019. "Probability models for data-Driven global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 40-57.
    8. Tabandeh, Armin & Sharma, Neetesh & Gardoni, Paolo, 2022. "Uncertainty propagation in risk and resilience analysis of hierarchical systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Wang, Yuhao & Pang, Yutian & Chen, Oliver & Iyer, Hari N. & Dutta, Parikshit & Menon, P.K. & Liu, Yongming, 2021. "Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    10. Pence, Justin & Sakurahara, Tatsuya & Zhu, Xuefeng & Mohaghegh, Zahra & Ertem, Mehmet & Ostroff, Cheri & Kee, Ernie, 2019. "Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 240-260.
    11. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    12. Perrin, G., 2016. "Active learning surrogate models for the conception of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 130-136.
    13. Qian, Gengjian & Massenzio, Michel & Brizard, Denis & Ichchou, Mohamed, 2019. "Sensitivity analysis of complex engineering systems: Approaches study and their application to vehicle restraint system crash simulation," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 110-118.
    14. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Kim, Yochan & Park, Jinkyun, 2019. "Incorporating prior knowledge with simulation data to estimate PSF multipliers using Bayesian logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 210-217.
    16. Sohoin, Rodrigue & El Hami, Abdelkhalak & Guerin, Fabrice & Riahi, Hassen & Attaf, Djelali, 2021. "A novel approach based on meta-modeling technique and time transformation function for reliability analysis of upgraded automotive components," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    17. Kapusuzoglu, Berkcan & Mahadevan, Sankaran, 2021. "Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    18. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    19. Cai, Wei & Zhao, Jingyi & Zhu, Ming, 2020. "A real time methodology of cluster-system theory-based reliability estimation using k-means clustering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    20. Li, Zhixiong & Wu, Dazhong & Hu, Chao & Terpenny, Janis, 2019. "An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 110-122.
    21. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    22. Yang, Xue & Ramezani, Ramin & Utne, Ingrid Bouwer & Mosleh, Ali & Lader, PÃ¥l Furset, 2020. "Operational limits for aquaculture operations from a risk and safety perspective," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    23. Sakurahara, Tatsuya & Schumock, Grant & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 84-99.
    24. Chao Hu & Byeng D. Youn & Pingfeng Wang, 2019. "Case Studies: Prognostics and Health Management (PHM)," Springer Series in Reliability Engineering, in: Engineering Design under Uncertainty and Health Prognostics, chapter 0, pages 303-342, Springer.
    25. Ray, Douglas & Ramirez-Marquez, Jose, 2020. "A framework for probabilistic model-based engineering and data synthesis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    26. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    27. Blancke, Olivier & Tahan, Antoine & Komljenovic, Dragan & Amyot, Normand & Lévesque, Mélanie & Hudon, Claude, 2018. "A holistic multi-failure mode prognosis approach for complex equipment," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 136-151.
    28. John H Lagergren & John T Nardini & Ruth E Baker & Matthew J Simpson & Kevin B Flores, 2020. "Biologically-informed neural networks guide mechanistic modeling from sparse experimental data," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-29, December.
    29. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    30. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
    31. Wang, Wei & Maio, Francesco Di & Zio, Enrico, 2017. "Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 276-289.
    32. Wang, Yuhao & Gao, Yi & Liu, Yongming & Ghosh, Sayan & Subber, Waad & Pandita, Piyush & Wang, Liping, 2021. "Bayesian-entropy gaussian process for constrained metamodeling," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    33. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    34. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    35. Shi, Lei & Lin, Shih-Po, 2016. "A new RBDO method using adaptive response surface and first-order score function for crashworthiness design," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 125-133.
    36. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    37. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    38. Bourinet, J.-M., 2016. "Rare-event probability estimation with adaptive support vector regression surrogates," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 210-221.
    39. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    40. Wu, Ji-Peng & Kang, Rui & Li, Xiao-Yang, 2020. "Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    41. Zywiec, William J. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2021. "Analysis of process criticality accident risk using a metamodel-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    42. Paolo Gardoni, 2017. "Risk and Reliability Analysis," Springer Series in Reliability Engineering, in: Paolo Gardoni (ed.), Risk and Reliability Analysis: Theory and Applications, pages 3-24, Springer.
    43. Li, Sai & Fang, Huajing & Shi, Bing, 2021. "Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    44. Schöbi, Roland & Sudret, Bruno, 2019. "Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 129-141.
    45. Guo, Jingbo & Wang, Changxi & Cabrera, Javier & Elsayed, Elsayed A., 2018. "Improved inverse Gaussian process and bootstrap: Degradation and reliability metrics," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 269-277.
    46. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    47. Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    48. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
    49. Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.
    50. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2021. "An Integrated Data and Knowledge Model Addressing Aleatory and Epistemic Uncertainty for Oil Condition Monitoring," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    51. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.
    52. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    53. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    54. Baptista, Marcia & Henriques, Elsa M.P. & de Medeiros, Ivo P. & Malere, Joao P. & Nascimento, Cairo L. & Prendinger, Helmut, 2019. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 228-239.
    55. Tan, Zhixue & Zhong, Shisheng & Lin, Lin, 2019. "Trans-layer model learning: A hierarchical modeling strategy for real-time reliability evaluation of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 120-132.
    56. Xie, Chaoyang & Li, Guijie & Wei, Fayuan, 2018. "An integrated QMU approach to structural reliability assessment based on evidence theory and kriging model with adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 112-122.
    57. Jain, Amit Kumar & Lad, Bhupesh Kumar, 2020. "Prognosticating RULs while exploiting the future characteristics of operating profiles," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    58. Jiang, Chen & Vega, Manuel A. & Todd, Michael D. & Hu, Zhen, 2022. "Model correction and updating of a stochastic degradation model for failure prognostics of miter gates," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    59. Chiachío, Juan & Chiachío, Manuel & Prescott, Darren & Andrews, John, 2019. "A knowledge-based prognostics framework for railway track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 127-141.
    60. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2020. "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    61. Whiteley, M & Dunnett, S & Jackson, L, 2020. "Simulation of polymer electrolyte membrane fuel cell degradation using an integrated Petri Net and 0D model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    62. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.
    63. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    64. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    65. Gatelli, D. & Kucherenko, S. & Ratto, M. & Tarantola, S., 2009. "Calculating first-order sensitivity measures: A benchmark of some recent methodologies," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1212-1219.
    66. Veiga, Sébastien Da & Marrel, Amandine, 2020. "Gaussian process regression with linear inequality constraints," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    67. Favarò, Francesca M. & Saleh, Joseph H., 2018. "Application of temporal logic for safety supervisory control and model-based hazard monitoring," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 166-178.
    68. Vasilyev, A. & Andrews, J. & Dunnett, S.J. & Jackson, L.M., 2021. "Dynamic Reliability Assessment of PEM Fuel Cell Systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    69. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    70. Chahrour, Nour & Nasr, Mohamad & Tacnet, Jean-Marc & Bérenguer, Christophe, 2021. "Deterioration modeling and maintenance assessment using physics-informed stochastic Petri nets: Application to torrent protection structures," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    71. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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    18. 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).
    19. Wei, Pengfei & Liu, Fuchao & Tang, Chenghu, 2018. "Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 183-195.
    20. 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).

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