Abstract
Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product’s performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner’s ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.
Highlights
Modern products are developed to have good quality and high reliability
Accelerated degradation model is composed of both stress-acceleration model and degradation model, where the stress-acceleration model describes the relationship between the levels of accelerating stress and the degradation rate, and the degradation model depicts the evolution of degradation process over time
In order to address the dependency among multiple degradation processes, a copula function can be applied, and the best model can be selected based on Akaike Information Criterion (AIC)
Summary
Modern products are developed to have good quality and high reliability. For some safety-critical components and systems, they are even designed to last for an extremely long time to avoid the catastrophic consequences of potential failures. Nonlinearity is quite natural due to the complex structures and failure mechanisms of the products To overcome this obstacle, some transformation methods for degradation data have been used, e.g., time-scale transformation [9,10,11] and log-transformation [12,13,14]. Sari et al [26] introduced a copula function to describe the correlation between two performance parameters, and combined it with a generalized linear model for bivariate constant-stress degradation data. This paper is aimed at making an early attempt to model s-dependent multivariate ADT data using general Wiener process and copulas. The remainder of this paper is organized as follows: Section 2 presents the univariate ADT model based on the general Wiener process and its parameter estimation method.
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