A control-guided failure restoration framework for the design of resilient engineering systems. (October 2018)

• Record Type: Journal Article
• Authors: Yodo, Nita; Wang, Pingfeng
• Is Part Of: Reliability engineering & system safety. Volume 178(2018)
• Page Start: 179
• Page End: 190
• Publication Date: 2018-10
• Subjects: Reliability -- Resilience -- Failure restoration -- Control theory -- Implicit systems -- Engineering design; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.05.018
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Presented a new framework for the design of resilience engineering systems. Developed a new control theory guided failure restoration mechanism in design. Integrated dynamic system modeling, control and resilience analysis for design. Employed an AI-based technique for implicit system dynamics modeling. Conducted a case study of power transmission system design for resilience. Abstract: When failures are inevitable, a resilient system is expected to restore ideal performance in a timely manner. The resilience of a system can be improved by enhancing the post-failure restoration ability of the system. In order to determine whether resilience in a system is sufficient towards a certain failure, a set of design parameters and performance equations describing the system behavior are essential in performing a resilience assessment. However, in implicit system applications, one of the main concerns is that there are no clearly defined system equations to describe system performance. To overcome this challenge, this paper presents a control-guided failure restoration (CGFR) framework, which combines dynamic system modeling and resilience analysis. Since there are no clearly defined system equations in implicit systems, the dynamic system modeling in the proposed framework is equipped with an artificial neural network to learn system behaviors. To demonstrate the feasibility of the proposed approach, a power transmission system is employed as a case study. The presented study aims to encourage the development of advanced failure restoration strategies for resilient engineered systems.