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Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks
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Cited by:
- Yang, Ao & Qiu, Qingan & Zhu, Mingren & Cui, Lirong & Chen, Weilin & Chen, Jianhui, 2022. "Condition-based maintenance strategy for redundant systems with arbitrary structures using improved reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Lee, Dongkyu & Song, Junho, 2023. "Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
- Morato, P.G. & Andriotis, C.P. & Papakonstantinou, K.G. & Rigo, P., 2023. "Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Yeter, B. & Garbatov, Y. & Guedes Soares, C., 2022. "Life-extension classification of offshore wind assets using unsupervised machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
- Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
- Azizi, Fariba & Salari, Nooshin, 2023. "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
- Wang, Jingjing & Qiu, Qingan & Wang, Huanhuan & Lin, Cong, 2021. "Optimal condition-based preventive maintenance policy for balanced systems," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- Hamida, Zachary & Goulet, James-A., 2023. "Hierarchical reinforcement learning for transportation infrastructure maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- 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).
- Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Kang, Renwei & Wang, Junfeng & Chen, Jianqiu & Zhou, Jingjing & Pang, Yanzhi & Guo, Longlong & Cheng, Jianfeng, 2022. "A method of online anomaly perception and failure prediction for high-speed automatic train protection system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
- Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
- Guan, Xiaoshu & Sun, Huabin & Hou, Rongrong & Xu, Yang & Bao, Yuequan & Li, Hui, 2023. "A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
- Tseremoglou, Iordanis & Santos, Bruno F., 2024. "Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- 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).
- Oakley, Jordan L. & Wilson, Kevin J. & Philipson, Pete, 2022. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
- Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
- Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
- Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Zheng, Meimei & Su, Zhiyun & Wang, Dong & Pan, Ershun, 2024. "Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Tao, Xin & Mårtensson, Jonas & Warnquist, Håkan & Pernestål, Anna, 2022. "Short-term maintenance planning of autonomous trucks for minimizing economic risk," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
- Zhang, Lin & Chen, Xiaohui & Khatab, Abdelhakim & An, Youjun, 2022. "Optimizing imperfect preventive maintenance in multi-component repairable systems under s-dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
- Gan, Shuyuan & Hu, Hengheng & Coit, David W., 2023. "Maintenance optimization considering the mutual dependence of the environment and system with decreasing effects of imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Zhang, Chunfang & Wang, Liang & Bai, Xuchao & Huang, Jianan, 2022. "Bayesian reliability analysis for copula based step-stress partially accelerated dependent competing risks model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
- Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).