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In recent years, accelerated destructive degradation testing (ADDT) has been applied to obtain the reliability information of an asset (component) at use conditions when the component is highly reliable. In ADDT, degradation data are measured under stress levels more severe than usual so that more component failures can be observed in a short period. In the literature, most application-specific ADDT models assume a parametric degradation process under different accelerating conditions. Models without strong parametric assumptions are desirable to describe the complex ADDT processes. This paper proposes a general ADDT model that consists of a nonparametric part to describe the degradation path and a parametric part to describe the accelerating-variable effect. The proposed model not only provides more model flexibility with few assumptions, but also retains the physical mechanisms of degradation. Due to the complexity of parameter estimation, an efficient method based on self-adaptive differential evolution is developed to estimate model parameters. A simulation study is implemented to verify the developed methods. Two real-world case studies are conducted, and the results show the superior performance of the developed model compared with the existing methods.
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Degradation under dynamic operating conditions: Modeling, competing processes and applications
This paper investigates degradation modeling under dynamic conditions and its applications. Both univariate and multiple competing degradation processes are considered with individual degradation paths being described by Wiener processes. Parametric and non-parametric approaches are used to capture the effect of environmental conditions on process parameters. For competing degradation processes, we obtain the probability that a particular process reaches a pre-defined threshold, before other processes, over future time intervals. In particular, we consider the potential statistical dependence among the latent remaining lifetimes of multiple degradation processes due to unobserved future environmental factors. Two case studies, aircraft piston pump wear and US highway performance deterioration, are presented. Comprehensive comparison studies are also performed to generate some critical insights on the proposed approach. Data have been made available on GitHub.
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- Award ID(s):
- 1904165
- PAR ID:
- 10166360
- Date Published:
- Journal Name:
- Journal of Quality Technology
- ISSN:
- 0022-4065
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/00224065.2020.1757390
