文献类型：期刊文献

英文题名：Software-hardware embedded system reliability modeling with failure dependency and masked data

作者：Zheng, Zhoutao Yang, Jianfeng Huang, Jiayue

第一作者：Zheng, Zhoutao

通信作者：Yang, JF[1]

机构：[1]Guizhou Univ, Sch Math & Stat, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Peoples R China;[3]Guizhou Univ, Guiyang 550025, Peoples R China;[4]Guian Kechuang Supercomp Power Algorithm Lab, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Sch Math & Stat, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Peoples R China.|贵州理工学院;

年份：2023

卷号：186

外文期刊名：COMPUTERS & INDUSTRIAL ENGINEERING

收录：;EI(收录号：20234715080702);Scopus(收录号：2-s2.0-85176960235);WOS:【SCI-EXPANDED(收录号:WOS:001113705600001)】；

基金：This work was supported by National Natural Science Foundation of China (No. 72361008, No. 71901078) , and Key Laboratory of Electric Power Big Data of Guizhou Province (No. QianKeHeJiZ [2015] 4001) .

语种：英文

外文关键词：System reliability; Failure dependency; Masked data; Copula function; IPSO-ELA algorithm

摘要：Traditional system reliability models often ignore failure dependency between subsystems and the existence of system failure masked data, thereby they can't accurately reflect the reliability modeling analysis of the whole system. This paper investigates an embedded system that comprises software subsystems and hardware sub-systems. In order to more accurately assess the embedded system's reliability, we proposed a reliability super-position model of the software-hardware system with masked data and failure dependency. In the model, the influence of masked failure data and failure dependency between the hardware subsystem and software sub-system is considered in system reliability evaluation, and the influence of fault dependence is solved by Copula function. Estimating the parameters of this model is a challenging task due to the complexity of the parameters. With regards to this, we proposed an immune particle swarm optimization algorithm with enhanced learning ability (IPSO-ELA), which is used to calculate 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. It can be seen from the experimental fitting results and the reliability trend charts that the system reliability when failure dependence is considered is higher than isn't considered, which is more realistic.