串聯系統元件壽命服從韋伯分配下之定應力破壞性加速壽命試驗分析;Constant Stress Accelerated Life Tests forDestructive Device of Series Components under Weibull Lifetime Distributions

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/54283

Title:	串聯系統元件壽命服從韋伯分配下之定應力破壞性加速壽命試驗分析;Constant Stress Accelerated Life Tests forDestructive Device of Series Components under Weibull Lifetime Distributions
Authors:	蘇瓔漪;Su,Ying-Yi
Contributors:	統計研究所
Keywords:	定應力加速壽命試驗;串聯系統;破壞性元件;韋伯分佈;貝氏方法;constant stress ALT;series system;destructive device;Weibull distribution;Bayesian approach.
Date:	2012-06-26
Issue Date:	2012-09-11 18:43:04 (UTC+8)
Publisher:	國立中央大學
Abstract:	本文考慮物件壽命為獨立韋伯分配的串聯系統之定應力加速壽命試驗, 其中韋伯分配之尺度參數和應力變數水準具對數線性關係。首先, 我們考慮在型I 設限下之三物件串聯系統的完全資料模型, 即觀測資料為系統之失效時間, 但是其中引起系統失效的物件不一定可被觀察到。若該物件無法被觀察到, 稱此資料為隱蔽資料。我們進一步的將模型推廣到破壞性元件之串聯系統中, 在給定應力水準下, 我們在特定時間測試每個串聯元件, 觀察在該時間時, 元件是否仍在正常運作中, 因此觀察到的資料為群集資料和可能引起失效的物件。當資料被隱蔽時, 我們以期望值-最大化演算法和拔靴法去做最大概似法之推論。另外, 我們也使用貝氏推論, 其中先驗分配來自關於可靠度之主觀先驗資訊。模擬結果顯示在資料大部份被隱蔽時, 貝氏分析之結果優於最大概似法。In this thesis, we consider modeling the constant stress accelerated life test of a series systemwith independent Weibull lifetime components whose scale parameters are log-linear in thelevels of the stress variable. We first consider a three-component series system where thesystem lifetime is collected under Type I censoring but the component that causes the systemto fail may or may not be observed. The data are so called masked for the latter case. Wethen extend the model to the destructive (also called by one-shot) device of series componentswhere each device is tested at specific time under given stress level. The data observed areinterval data for the device systems with the associated failed components if observed. Whenthe data are masked, EM algorithm and the bootstrap method are used to draw the maximumlikelihood inference. Bayesian approach incorporated with subjective priors on the reliabilityis also applied. Simulation study shows that Bayesian analysis outperforms the maximumlikelihood approach especially when the data are highly masked.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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