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Software-Hardware Embedded System Reliability Modeling with Failure Dependency and Masked Data

出版	SSRN, 2023
URL	http://books.google.com.hk/books?id=NrRG0AEACAAJ&hl=&source=gbs_api

註釋Abstract: Traditional system reliability models often ignore failure dependencies between subsystems and cannot reflect the reliability modeling analysis of the whole system. This paper investigates an embedded system that comprises a hardware system and a software system. To account for the influence of masked failure data and failure dependency between the hardware subsystem and software subsystem in system reliability evaluation, and a Copula function is employed. We proposed a reliability superposition model of the software-hardware system with masked data and failure dependency. Estimating the parameters of this model is a challenging task due to the complexity of the parameters. General methods may not be suitable for solving the estimated values of the parameters of the proposed model. To address this issue, we proposed an immune particle swarm optimization algorithm with enhanced learning ability (IPSO-ELA). The resulting algorithm, IPSO-ELA, is used to approximate the parameter's estimation. Additionally, we investigated the impact of varying degrees of failure dependence on system reliability. Finally, the numerical experiment shown that the proposed model, which considers failure dependency among subsystems, outperforms other reliability models that do not. The fitting results and reliability trend chart confirm this superiority.