摘要：Resilience is crucial for systems to maintain functionality under disturbances, especially in critical applications. However, current methods for assessing resilience in multi-state systems (MSS), particularly those modeled with Markov Repairable Processes (MRP), often face high computational costs and inefficiencies in handling complex dynamics. To address these issues, this paper proposes a systematic framework for resilience assessment of MSS whose recovery process is described as a MRP, integrated with enhanced Physics-Informed Neural Networks (PINN). In the first step of the framework, the computation of resilience indices is performed, based on the MRP of the MSS and considering the system evolution through vulnerable and recovery phases. In the second step of the framework, the enhanced PINN is integrated into the MRP solution. A typical standby MSS structure is analyzed based on the proposed framework. By gradient calibration and momentum-driving training, the computational cost is shown to be reduced by 92.4 %, compared to the eigenvector method of solution. The approach is adaptable to other safety-critical systems, offering a robust tool for more effective resilience evaluation and system optimization. ? 2025 Elsevier Ltd

ISSN号：0951-8320

卷、期、页：卷257子辑B

发表日期：2025-05-01

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Reliability Engineering and System Safety

参与作者：恩里克齐奥,范霖,张宗杰

通讯作者：何宇轩,向旗,何倩,彭世亮

第一作者：杨兆铭,苏怀,张劲军

论文类型：期刊论文

论文概要：何宇轩,恩里克齐奥,杨兆铭,向旗,范霖,何倩,彭世亮,张宗杰,苏怀,张劲军，A systematic resilience assessment framework for multi-state systems based on physics-informed neural network，Reliability Engineering and System Safety，2025，卷257子辑B

论文题目：A systematic resilience assessment framework for multi-state systems based on physics-informed neural network