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Modernizing risk assessment: A systematic integration of PRA and PHM techniques

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  • Moradi, Ramin
  • Groth, Katrina M.

Abstract

Recent advances in sensing and computing technologies have resulted in an abundance of data in various formats and more processing power for using this data. Consequently, there has been an interest in using these advances to enhance modeling and assessment techniques for safety and reliability of a variety of systems. To date, this has occurred under two distinct aspects of reliability engineering. Prognostics and Health Management (PHM) has developed powerful new algorithms for understanding and predicting the mechanical and electrical devices’ health. For complex systems, the techniques of Probabilistic Risk Assessment (PRA), which provide a system-level perspective, have become increasingly dynamic. Both PHM and PRA bring unique advantages and limitations. PHM excels at data handling and supports prediction, but the methods applicable at the component level are not suitable for modeling complex engineering systems (CES). PRA provides a comprehensive approach suitable for drawing together many types of data and assessing complex systems, but is limited in the ability to exploit advanced ML methods or enable prediction. In this paper, we explore how to systematically draw together the advances in PHM and PRA to provide a more forward-looking, model- and data-driven approach for assessing and predicting the risk and health of CES.

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  • 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).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306955
    DOI: 10.1016/j.ress.2020.107194
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    References listed on IDEAS

    as
    1. Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
    2. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    4. Favarò, Francesca M. & Saleh, Joseph H., 2016. "Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 316-330.
    5. Abu-Samah, A. & Shahzad, M.K. & Zamai, E., 2017. "Bayesian based methodology for the extraction and validation of time bound failure signatures for online failure prediction," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 616-628.
    6. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    7. Xiaolei Fang & Nagi Z. Gebraeel & Kamran Paynabar, 2017. "Scalable prognostic models for large-scale condition monitoring applications," IISE Transactions, Taylor & Francis Journals, vol. 49(7), pages 698-710, July.
    8. Khorasgani, Hamed & Biswas, Gautam & Sankararaman, Shankar, 2016. "Methodologies for system-level remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 8-18.
    9. 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.
    10. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    11. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    12. 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.
    13. Katrina M Groth & Matthew R Denman & Michael C Darling & Thomas B Jones & George F Luger, 2020. "Building and using dynamic risk-informed diagnosis procedures for complex system accidents," Journal of Risk and Reliability, , vol. 234(1), pages 193-207, February.
    14. 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.
    15. Yuan, Tao & Wu, Xinying & Bae, Suk Joo & Zhu, Xiaoyan, 2019. "Reliability assessment of a continuous-state fuel cell stack system with multiple degrading components," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 157-164.
    16. Andrés Ruiz-Tagle Palazuelos & Enrique López Droguett & Rodrigo Pascual, 2020. "A novel deep capsule neural network for remaining useful life estimation," Journal of Risk and Reliability, , vol. 234(1), pages 151-167, February.
    17. Taleb-Berrouane, Mohammed & Khan, Faisal & Amyotte, Paul, 2020. "Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    18. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
    19. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne, 2016. "Remaining useful lifetime estimation and noisy gamma deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 76-87.
    20. Costa, Rodrigo & Haukaas, Terje & Chang, Stephanie E. & Dowlatabadi, Hadi, 2019. "Object-oriented model of the seismic vulnerability of the fuel distribution network in coastal British Columbia," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 11-23.
    21. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    22. Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
    23. Byun, Ji-Eun & Zwirglmaier, Kilian & Straub, Daniel & Song, Junho, 2019. "Matrix-based Bayesian Network for efficient memory storage and flexible inference," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 533-545.
    24. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
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    8. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
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    15. 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).
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