Optimal design for step-stress accelerated degradation testing based on D-optimality

Ge, Zhengzheng; Li, Xiaoyang; Jiang, Tongmin; Huang, Tingting

doi:10.1109/rams.2011.5754481

Cited by 17 publications

(15 citation statements)

References 10 publications

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“…When the multivariate normal distribution function is adopted, the parameters are the mean vector and the covariance matrix. Then the left side of (18) can be substituted by (17). The pth percentile lifetime can be obtained by solving this simple equation.…”

Section: Objective Functionmentioning

confidence: 99%

“…10 The stochastic process model is used to describe the degradation process, and has many advantages. Methods have been developed for optimal design of ADT based on stochastic process models, such as inverse Gaussian process, 11,12 Wiener process, [13][14][15] drift Brownian motion process, 16,17 and Gamma process. [18][19][20] These methods are studied for cases with a single stress or single degradation measure.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of Step‐stress Accelerated Degradation Test with Multiple Stresses and Multiple Degradation Measures

Wang

Chen

Tan

2017

Quality & Reliability Eng

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Products with high reliability and long lifetimes undergo different types of stresses in use conditions. Often there are multiple performance indicators for products that gradually degrade over time. An accelerated degradation test (ADT) with multiple stresses and multiple degradation measures (MSMDM) may provide a more accurate prediction of the lifetime of these products. However, the ADT requires a moderate sample size, which is not practical for newly developed or costly products with only a few available test specimens on hand. Therefore, in this study, a step‐stress ADT (SSADT) with MSMDM is developed. However, it is a difficult endeavor to design SSADT with MSMDM to predict accurate reliability estimation under several constraints. Previous methods are used only for cases with a single stress or degradation measure, and are not suitable for SSADT with MSMDM. In this paper, an approach of optimal design is proposed for SSADT with MSMDM and its steps for a rubber sealed O‐ring are demonstrated to illustrate its validity. Results of the sensitivity analysis for the optimal test plan indicate robustness when the deviation of model parameters is within 10% of the estimated values. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Design of Step‐stress Accelerated Degradation Test with Multiple Stresses and Multiple Degradation Measures

Wang

Chen

Tan

2017

Quality & Reliability Eng

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“…The optimal design method for ADT is different for different optimization objectives. In order to improve the fitting accuracy of the degradation model, Ge et al [8] optimized the step stress accelerated degradation test (SSADT) using the Wiener process by maximizing the determinant of the Fisher information matrix, which is also called D-optimization. Ye et al [9] optimized the constant stress accelerated degradation test (CSADT) using the stochastic parameters' Inverse Gauss (IG) process by minimizing the asymptotic variance of the P-quantile of life distribution under the normal stress (i.e., V-optimization), which improves the prediction accuracy of degradation model.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of Constant Stress Accelerated Degradation Test Using Inverse Gaussian Process

Guo

et al. 2019

IEEE Access

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A multi-objective optimization method for constant stress accelerated degradation test is proposed in order to solve the problem of different or even conflicted test configuration under different optimization objectives. The inverse Gaussian process is used as a degradation model, and the unknown parameters are solved by maximum likelihood estimation. The two optimization criteria of the maximum determinant of the information matrix and the minimum asymptotic variance of P-quantile are considered. The improved multi-objective particle swarm optimization algorithm is proposed to search test optimal configuration, and the Pareto solution set for dual-objectives is obtained. Finally, the effectiveness of the method is illustrated by a group of examples of electrical connectors. Compared with the single-objective optimization design, the proposed method is more reasonable and convenient for test configuration. The performance index of the test function indicates that the optimal algorithm we proposed has some obvious advantages over the NSGA-II in diversity and convergence of the Pareto solutions and it is significant in guiding engineering practice. INDEX TERMS Accelerated degradation test, optimal design, multi-objective, maximum likelihood estimation, multi-objective particle swarm optimization.

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“…The model is very suitable to describe a time-dependent degradation process in which error terms cannot be assumed to be independent identically normally distributed. Several methods have been developed for ADT based on stochastic process models, such as inverse Gaussian process [16,22], Wiener process [5,6,17], drift Brownian motion process [4,28], and Gamma process [7,18,27]. These methods are mainly from statistical perspective and lacking in support of physical rules; thus, the prediction accuracy depends on sample size and model selection.…”

Section: Introductionmentioning

confidence: 99%

Lifetime prediction of self-lubricating spherical plain bearings based on physics-of-failure model and accelerated degradation test

Wang¹,

Fang²,

Zhang³

et al. 2016

EiN

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Optimal design for step-stress accelerated degradation testing based on D-optimality

Cited by 17 publications

References 10 publications

Optimal Design of Step‐stress Accelerated Degradation Test with Multiple Stresses and Multiple Degradation Measures

Optimal Design of Step‐stress Accelerated Degradation Test with Multiple Stresses and Multiple Degradation Measures

Multi-Objective Optimization Design of Constant Stress Accelerated Degradation Test Using Inverse Gaussian Process

Lifetime prediction of self-lubricating spherical plain bearings based on physics-of-failure model and accelerated degradation test

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