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- Lin, Jing, et al.
(författare)
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A Cure Rate Model in Reliability for Complex System
- 2008
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Ingår i: 2008 IEEE International Conference on Industrial Engineering and Engineering Management. - : IEEE Communications Society. - 9781424426300 ; , s. 1395-1399
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new approach to do reliability analysis for complex system, where a certain fraction of the subsystems is defined as a ¿cure fraction¿ under the consideration that such subsystems are ¿longevous¿ compared with the entire system. Including introducing environment covariates and the joint power prior, the proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters¿ posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.
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- Lin, Jing, et al.
(författare)
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Bayesian analysis for randomly truncated constant-stress accelerated life testing
- 2007
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Ingår i: Journal of Systems Engineering and Electronics. - 1001-506X. ; 29:2, s. 320-323
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Tidskriftsartikel (refereegranskat)abstract
- Aimed at the fault of the traditional numeration methods, the Weibull model, which is used widely in the family of Bayesian accelerated failure-time model was discussed. Markov chain Monte Carlo method based on Gibbs sampling was discussed, which were used to simulate dynamically the Markov Chain of the parameters’ posterior distribution. Also, the parameters’ Bayesian estimations were given out with prior suppose for its parameters. What’s more, the results of the data’s simulation were utilized to show the process of setting the model by using the BUGS package. It proves the objectivity and validity of the model.
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- Lin, Jing, et al.
(författare)
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Bayesian survival analysis in reliability for complex system with a cure fraction
- 2011
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Ingår i: International Journal of Performability Engineering. - 0973-1318. ; 7:2, s. 109-120
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Tidskriftsartikel (refereegranskat)abstract
- In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model
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