4.6 Article

A Random-Field-Environment-Based Multidimensional Time-Dependent Resilience Modeling of Complex Systems

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出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2021.3083515

关键词

Resilience; Reliability; Maintenance engineering; System performance; Reliability engineering; Biological system modeling; Uncertainty; Random field environment (RFE); resilience; uncertainty; vehicular edge computing network (VECN)

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This article explores resilience concept in the engineering field, introducing a multidimensional time-dependent resilience model based on random field environment, which connects reliability, vulnerability, and recoverability, while considering specific functions of the impact of the RFE on system performance and recovery.
Over the past few decades, many research efforts have been dedicated to qualitatively and quantitatively evaluate resilience in different domains. As compared with research areas in social science and ecology, the concept of resilience in the engineering domain is relatively new. In the engineering domain, studies on resilience mostly focus on civil infrastructure. It is important to extend the concept of resilience to a broad range of engineering applications. The field environments of complex engineering systems vary with different applications. Even with the same component/system applied in different field environments, the ability, time, and resources required by failure detection, diagnosis, and restoration can be different. Hence, it is critical to introduce a new dimension, random field environment (RFE), into the development of the mathematical model for quantifying resilience. This article first introduces a new definition of resilience and then proposes a general RFE-based multidimensional time-dependent resilience model connecting reliability, vulnerability, and recoverability. Besides, we present a specific RFE-based multidimensional time-dependent resilience model by considering the specified functions of the impact of the RFE on system performance and recovery. Furthermore, we extend the proposed resilience model by incorporating multiple failure paths of complex systems. Finally, we apply the proposed resilience model to vehicular edge computing networks to evaluate the vehicular network resilience with the disruptive events on the communication links.

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