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Degradation modeling of 2 fatigue‐crack growth characteristics based on inverse Gaussian processes: A case study

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  • Luis Alberto Rodríguez‐Picón
  • Anna Patricia Rodríguez‐Picón
  • Alejandro Alvarado‐Iniesta

Abstract

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.

Suggested Citation

  • Luis Alberto Rodríguez‐Picón & Anna Patricia Rodríguez‐Picón & Alejandro Alvarado‐Iniesta, 2019. "Degradation modeling of 2 fatigue‐crack growth characteristics based on inverse Gaussian processes: A case study," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 504-521, May.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:3:p:504-521
    DOI: 10.1002/asmb.2329
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    Cited by:

    1. Song, Kai & Cui, Lirong, 2022. "A common random effect induced bivariate gamma degradation process with application to remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Song, Kai & Shi, Jian & Yi, Xiaojian, 2020. "A time-discrete and zero-adjusted gamma process model with application to degradation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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