Fusion Assessment of Safety and Security for Intelligent Industrial Unmanned Systems

• Published in: 2024 7th International Symposium on Autonomous Systems (ISAS) pp. 1 - 6
• Main Authors: Cai, Rongyao, Xv, Xiao, Lu, Zhengming, Zhang, Kexin, Liu, Yong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.05.2024
• Subjects: Attack tree; Autonomous systems; Binary decision diagrams; Control systems; Couplings; Dynamic markov models; Fault tree analysis; Maintenance engineering; Pressure vessels; Process control; Safety
• DOI: 10.1109/ISAS61044.2024.10552597

Fault tree analysis is the most commonly used methodology in industrial safety analysis to predict the probability or frequency of system failure. Although fault tree analysis has been proposed for more than six decades, the assumptions used in most commercial fault tree analysis codes have not changed significantly, which limits the ability of the method to represent design, operation, and maintenance characteristics in the context of the increasing complexity and specialization of modern industrial systems. The basic setup of traditional fault trees is unable to include dependencies between events, time-varying failures, and repair rate realities to explain complex maintenance strategies. To address the above shortcomings, we propose a fusion tree model combining fault tree and attack tree, and simplify the causal structure of the fusion tree by modularization, and utilize the dynamic Markov model to represent the complex coupling relationship between components or nodes. Finally, we demonstrate the calculation process of fusion tree in pressure vessel systems with temporal control.