Predictive resilience analysis of complex systems using dynamic Bayesian networks

No Thumbnail Available
Authors
Yodo, Nita
Wang, Pingfeng
Zhou, Zhi
Advisors
Issue Date
2017-09
Type
Article
Keywords
Complex systems , Dynamic Bayesian network (DBN) , Dynamic systems , Engineering resilience
Research Projects
Organizational Units
Journal Issue
Citation
N. Yodo, P. Wang and Z. Zhou, "Predictive Resilience Analysis of Complex Systems Using Dynamic Bayesian Networks," in IEEE Transactions on Reliability, vol. 66, no. 3, pp. 761-770, Sept. 2017
Abstract

Uncertain and potentially harsh operating environments are often known to alter the operational performance of a system. In order to maintain system performancewhile coping with varying operating environments and potential disruptions, the resilience of engineered systems is desirable. Engineering systems are often interconnected in a dimensional way inherently from basic components to subsystems to the system of systems, which poses a grand challenge for system designers to analyze the resilience of such a complex system. Moreover, further complications in the assessment of resilience in the engineering domain are attributed to time-varying system performances, random perturbation occurrences, and probable failures caused by adverse events. This paper presents a dynamic Bayesian network (DBN) approach for the modeling and predictive resilience analysis for dynamic engineered systems. With the inter-time-slice links and the conditional probability tables in a DBN, the system performance could be molded as changing in a discrete time slice, while capturing the temporal probabilistic dependencies between the variables. An industrialbased case study of an electricity distribution system is further studied to demonstrate the effectiveness of the DBN approach for resilience analysis. The approach presented in this paper hopes to aid in realizing resiliency in system designs and to pave the way toward enhancements in developing resilient engineered systems.

Table of Contents
Description
Click on the DOI link to access the article (may not be free).
Publisher
IEEE
Journal
Book Title
Series
IEEE Transactions on Reliability;v.66:no.3
PubMed ID
DOI
ISSN
0018-9529
EISSN