Modelling degradation with multiple accelerated processes. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Rodríguez-Picón, Luis A.; Rodríguez Borbón, Manuel I.; Valles-Rosales, Delia J.; Flores Ochoa, Víctor H.
• Is Part Of: Quality technology & quantitative management. Volume 13:Issue 3(2016)
• Page Start: 333
• Page End: 354
• Publication Date: 2016-07-02
• Subjects: Degradation model -- Accelerated testing -- Bayesian approach -- Arrhenius relationship -- inverse power law; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2016.1189202
• Languages: English
• ISSNs: 1684-3703

Abstract

Abstract: Multivariate degradation models are important tools for reliability assessment of highly reliable products. The multivariate aspect of the models depends on the device under study and the type of test applied to it. A device may exhibit different performance characteristics (PC) or one PC may be affected by multiple environmental conditions. If a device is affected by multiple environmental conditions, then it is possible to subject it to multiple accelerating variables in an accelerated degradation test in order to obtain reliability information. In this paper, two accelerating stress variables are considered, which characterize two degradation processes (DP) on a PC. In this way, two degradation models based on the life–stress Arrhenius relationship and the inverse power law relationship are provided. It is shown that the final distribution function characterizing the failure times for each DP can be used to estimate the reliability of a fuel sensor, the example under study in this paper. The interaction of the models with respect to the DPs is described via copula 1 function, in order to model the dependency structure. A Bayesian approach is proposed to estimate the parameters of the final models and the best fit models are selected using information criteria.