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Degradation modeling of 2 fatigue‐crack growth characteristics based on inverse Gaussian processes: A case study
Author(s) -
RodríguezPicón Luis Alberto,
RodríguezPicón Anna Patricia,
AlvaradoIniesta Alejandro
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2329
Subject(s) - inverse gaussian distribution , computer science , bivariate analysis , copula (linguistics) , reliability (semiconductor) , bayesian inference , degradation (telecommunications) , gaussian , bayesian probability , joint probability distribution , inverse , reliability engineering , econometrics , mathematics , statistics , distribution (mathematics) , engineering , artificial intelligence , machine learning , physics , quantum mechanics , geometry , mathematical analysis , power (physics) , telecommunications
Most modern products that are highly reliable are complex in their inner and outer structures. This situation indicates quality characterization by the interaction of multiple performance characteristics, which motivates the utilization of robust reliability models to obtain robust estimates. It is paramount to obtaining substantial information about a product's life cycle; therefore, when multiple performance characteristics are dependent, it is important to find models that address the joint distribution of performance degradation of such. In this paper, a reliability model for products with 2 fatigue‐crack growth characteristics related to 2 degradation processes is developed. The proposed model considers the dependence among degradation processes by using copula functions considering the marginal degradation processes as inverse Gaussian processes. The statistical inference is performed by using a Bayesian approach to estimate the parameters of the joint bivariate model. A time‐scale transformation is considered to assure monotone paths of the degradation trajectories. The comparison results of the reliability analysis, under both dependent and independent assumptions, are reported with the implementation of the proposed modeling in a case study, which consists of the crack propagation data of 2 terminals of an electronic device.