Dynamic Availability Assessment Using Dynamic Evidential Network: Water Deluge System Case Study. Issue 1 (1st October 2022)

• Record Type: Journal Article
• Authors: Bougofa, Mohammed; Bouafia, Abderraouf; Chakhrit, Ammar; Guetarni, I H M; Baziz, Amin; Aberkane, Salah; Zerouali, Bilal; Kharzi, Rabeh; Bellaouar, Ahmed
• Is Part Of: IOP conference series. Volume 1098:Issue 1(2022)
• Publication Date: 2022-10-01
• Subjects: Dempster-Shafer theory -- dynamic evidential network -- epistemic uncertainty -- availability -- common cause failure -- spare gates; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/1098/1/012015
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Probabilistic modeling is widespread in engineering practices, mainly to evaluate the safety, risk analysis, and reliability of complex systems. However, insufficient data makes it difficult to estimate the state probability of components or the global system in dynamic complex systems. Furthermore, conventional methods for dependability analysis typically have little capacity to cope with dependence, failure behavior, epistemic uncertainty, and common cause failure simultaneously. This paper presents the application of an extended discrete-time dynamic evidential network (DEN) model to assess the availability of complex systems. The model application combines Dempster-Shafer's theory to treat epistemic uncertainty over a new state-space reconstruction of components and the dynamic Bayesian network to present multi-state system dependability. This model is demonstrated in a real case study of a water deluge system installed as a safety barrier from Algeria's oil and gas plant. The results show the significant influence of these factors on the system's availability. The goal of this modeling is to assure the high availability of a safety barrier in a volatile setting by providing a decision-making tool to prioritize maintenance tasks, preventing the failure of complicated redundant systems, and recommending alterations to the design.