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Bayesian synthetic evaluation of multistage reliability growth with instant and delayed fix modes 被引量:5
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作者 Yan Zhiqiang Li Xinxin Xie Hongwei Jiang Yingjie 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第6期1287-1294,共8页
In the multistage reliability growth tests with instant and delayed fix modes, the failure data can be assumed to follow Weibull processes with different parameters at different stages. For the Weibull process within ... In the multistage reliability growth tests with instant and delayed fix modes, the failure data can be assumed to follow Weibull processes with different parameters at different stages. For the Weibull process within a stage, by the proper selection of prior distribution form and the parameters, a concise posterior distribution form is obtained, thus simplifying the Bayesian analysis. In the multistage tests, the improvement factor is used to convert the posterior of one stage to the prior of the subsequent stage. The conversion criterion is carefully analyzed to determine the distribution parameters of the subsequent stage's variable reasonably. Based on the mentioned results, a new synthetic Bayesian evaluation program and algorithm framework is put forward to evaluate the multistage reliability growth tests with instant and delayed fix modes. The example shows the effectiveness and flexibility of this method. 展开更多
关键词 reliability growth bayesian analysis improvement factor multistage test Weibull process
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Bayesian method for system reliability assessment of overlapping pass/fail data 被引量:4
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作者 Zhipeng Hao Shengkui Zeng Jianbin Guo 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期208-214,共7页
For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve th... For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps: fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach. 展开更多
关键词 system reliability assessment bayesian analysis limited samples overlapping pass/fail data
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Dependence Rayleigh competing risks model with generalized censored data 被引量:1
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作者 WANG Liang MA Jin’ge SHI Yimin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第4期852-858,共7页
The inference for the dependent competing risks model is studied and the dependent structure of failure causes is modeled by a Marshall-Olkin bivariate Rayleigh distribution. Under generalized progressive hybrid censo... The inference for the dependent competing risks model is studied and the dependent structure of failure causes is modeled by a Marshall-Olkin bivariate Rayleigh distribution. Under generalized progressive hybrid censoring(GPHC), maximum likelihood estimates are established and the confidence intervals are constructed based on the asymptotic theory. Bayesian estimates and the highest posterior density credible intervals are obtained by using Gibbs sampling. Simulation and a real life electrical appliances data set are used for practical illustration. 展开更多
关键词 dependence competing risks bivariate distribution generalized progressive hybrid censoring(GPHC) likelihood estimation bayesian analysis
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