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Bayesian survival analysis in reliability for complex system with a cure fraction
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
College of Business Administration, Hunan University.
2011 (English)In: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 7, no 2, p. 109-120Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
2011. Vol. 7, no 2, p. 109-120
National Category
Other Civil Engineering Signal Processing
Research subject
Operation and Maintenance; Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-3047Local ID: 0cd44760-e0e2-409b-8b30-5a45ecff899eOAI: oai:DiVA.org:ltu-3047DiVA, id: diva2:975903
Note
Validerad; 2011; 20110328 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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