Lifetime Inference for Highly Reliable Products Based on Skew-Normal Accelerated Destructive Degradation Test Model

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題名:	Lifetime Inference for Highly Reliable Products Based on Skew-Normal Accelerated Destructive Degradation Test Model
作者:	Tsai, C. C.;Lin, C. T.
關鍵詞:	Accelerated destructive degradation tests;expectation-maximization algorithm;highly reliable products;skew-normal distribution
日期:	2019-12-14
上傳時間:	2021-03-06 12:13:45 (UTC+8)
摘要:	The accelerated destructive degradation test (ADDT) method provides an effective way to assess the reliability information of highly reliable products whose quality characteristics degrade over time, and can be taken only once on each tested unit during the measurement process. Conventionally, engineers assume that the measurement error follows the normal distribution. However, degradation models based on this normality assumption often do not apply in practical applications. To relax the normality assumption, the skew-normal distribution is adopted in this study because it preserves the advantages of the normal distribution with the additional benefit of flexibility with regard to skewness and kurtosis. Here, motivated by polymer data, we propose a skew-normal nonlinear ADDT model, and derive the analytical expressions for the product's lifetime distribution along with its corresponding pth percentile. Then, the polymer data are used to illustrate the advantages gained by the proposed model.
顯示於類別:	[數學學系暨研究所] 會議論文

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