7 Papers
Yiming Chen is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Maintenance actions & Computer science. The author has an hindex of 2, co-authored 4 publications.
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Papers
Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach
Yu Liu,Yiming Chen,Tao Jiang +2 more
TL;DR: A new selective maintenance optimization for multi-state systems that can execute multiple consecutive missions over a finite horizon is developed and a customized deep reinforcement learning method is put forth to overcome the “curse of dimensionality” and mitigate the uncountable state space.
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On sequence planning for selective maintenance of multi-state systems under stochastic maintenance durations
Yu Liu,Yiming Chen,Tao Jiang +2 more
TL;DR: A new selective maintenance model for multi-state systems is developed to maximize the probability of a system successfully completing the next mission, while taking account of the stochasticity of the durations of breaks and maintenance actions.
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Optimal Maintenance Strategy for Multi-State Systems with Single Maintenance Capacity and Arbitrarily Distributed Maintenance Time
Yiming Chen,Yu Liu,Tao Jiang +2 more
TL;DR: In this article, a new maintenance optimization problem for multi-state systems with single maintenance capacity is studied, where the homogeneous continuous-time Markov process is used to characterize the deterioration of multi-State components in a system.
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Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty
Mingang Yin,Yu Liu,Shun-Yi Liu,Yiming Chen,Yutao Yan +4 more
TL;DR: In this paper , a new selective maintenance model with multiple heterogeneous repair channels is formulated to maximize the probability of a system successfully completing the next mission subject to a limited maintenance budget.
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Availability for multi-component k-out-of-n: G warm-standby system in series with shut-off rule of suspended animation
Linhan Guo,Ruiyang Li,Yu Wang,Jun Yang,Yu Liu,Yiming Chen,Jianguo Zhang +6 more
TL;DR: In this paper , the authors proposed an effective algorithm for the transition rates of the SA state is constructed to solve the three types of state probabilities, whose iteration begins with the same subsystem CTMC not considering the SA rule.
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