Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation
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DOI: 10.1016/j.ress.2022.108365
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- Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
- Bian, Chong & Yang, Shunkun & Huang, Tingting & Xu, Qingyang & Liu, Jie & Zio, Enrico, 2019. "Degradation state mining and identification for railway point machines," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 432-443.
- Zhang, Haoyuan & Marsh, D. William R, 2021. "Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
- Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
- Paul Fearnhead & Dennis Prangle, 2012. "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 419-474, June.
- Andrews, John & Fecarotti, Claudia, 2017. "System design and maintenance modelling for safety in extended life operation," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 95-108.
- Sheng, Jingyu & Prescott, Darren, 2019. "Using a novel hierarchical coloured Petri net to model and optimise fleet spare inventory, cannibalisation and preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
- Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
- Li, Xiao-Yang & Liu, Yue & Lin, Yan-Hui & Xiao, Liang-Hua & Zio, Enrico & Kang, Rui, 2021. "A generalized petri net-based modeling framework for service reliability evaluation and management of cloud data centers," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
- Yianni, Panayioti C. & Neves, Luis C. & Rama, Dovile & Andrews, John D., 2018. "Accelerating Petri-Net simulations using NVIDIA Graphics Processing Units," European Journal of Operational Research, Elsevier, vol. 265(1), pages 361-371.
- Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
- Latsou, Christina & Dunnett, Sarah J. & Jackson, Lisa M., 2019. "A new methodology for automated Petri Net generation: Method application," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 113-123.
- Chen, Yiming & Liu, Yu & Jiang, Tao, 2021. "Optimal maintenance strategy for multi-state systems with single maintenance capacity and arbitrarily distributed maintenance time," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- 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).
- 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).
- Mark A. Beaumont & Jean-Marie Cornuet & Jean-Michel Marin & Christian P. Robert, 2009. "Adaptive approximate Bayesian computation," Biometrika, Biometrika Trust, vol. 96(4), pages 983-990.
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Cited by:
- Andrews, John & Tolo, Silvia, 2023. "Dynamic and dependent tree theory (D2T2): A framework for the analysis of fault trees with dependent basic events," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Saleh, Ali & ChiachÃo, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Saleh, Ali & Remenyte-Prescott, Rasa & Prescott, Darren & ChiachÃo, Manuel, 2024. "Intelligent and adaptive asset management model for railway sections using the iPN method," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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Keywords
Petri nets; Model similarity; Bayesian inference; Approximate Bayesian Computation; Maintenance models;All these keywords.
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