Reliability analysis of IoV-based vehicle monitoring systems subject to cascading probabilistic common cause failures

• Published in: Reliability engineering & system safety Vol. 254; p. 110605
• Main Authors: Wang, Chaonan, Lie, Yingxi, Mo, Yuchang, Guan, Quanlong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Cascading effect; Internet of Things; Internet of Vehicle; Probabilistic common cause failure; Reliability analysis; Cascading effect; Reliability analysis; Internet of Vehicle; Probabilistic common cause failure; Internet of Things
• ISSN: 0951-8320
• DOI: 10.1016/j.ress.2024.110605

•A combinatorial method is proposed for reliability analysis of Internet of Vehicles based vehicle monitoring systems subject to cascading probabilistic common cause failures.•The proposed methods consider complex cascading effects of PCCFs which includes directed acyclic graph structure and Hamilton loop structure, and have no limitation on time-to-failure distributions for system devices.•The applications and advantages of the proposed methods are illustrated and the correctness of the methods is proved by Monte Carlo simulation. As an important application of the Internet of Things (IoT), Internet of Vehicles (IoV)-based vehicle monitoring systems (IVMSs), gathering, processing and communicating traffic and vehicle data, are installed in vehicles and deployed to avoid traffic accidents and ensure road safety. In this paper, the reliability of IVMSs subject to cascading probabilistic common cause failures (CPCCFs) is studied where a common cause (CC) may cause multiple system devices to fail probabilistically and the failures of some devices may further trigger failures of other system devices in a domino manner. Two combinatorial methods are proposed to handle complex cascading effects of directed acyclic graph structure and Hamilton loop structure, respectively. The proposed methods are applicable to any arbitrary time-to-failure distribution of devices and both external and internal CCs are considered. The applications and advantages of the proposed methods are illustrated through an IVMS example. The correctness of the methods is proved by Monte Carlo simulation. The time and space complexity of the methods is also analyzed.