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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65493


    Title: 串聯系統中元件壽命為指數分配之區間資料的階段加速壽命試驗;Step-Stress Accelerated Life Tests of Series Systems with Interval Data Under Exponential Lifetime Distributions
    Authors: 詹翔豪;Chan,Hsiang-Hao
    Contributors: 統計研究所
    Keywords: 群集資料;串聯系統;累積曝露模型;型I 設限;階段加速壽命試驗;Marshall-Olkin 二元指數分配;Interval data;Series system;Cumulative exposure model;Type-I censor;Stepstress accelerated degradation test;Marshall-Olkin bi-exponential distribution
    Date: 2014-07-22
    Issue Date: 2014-10-15 15:33:11 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在串聯系統中, 任一元件失效, 將導致系統無法運作, 有時無法確知造成系統失效的元件, 即為隱蔽資料。本文討論觀測值為區間資料之串聯系統, 其中各元件壽命服從指數分配且其平均壽命與應力間具對數線性關係, 並服從累積曝露模型。考慮型I 設限的階段加速壽命試驗, 分別在元件壽命分配彼此獨立及具Marshall-Olkin 二元指數分配下, 利用期望值-最大概似演算法及遺失資訊法則求得參數之最大概似估計與其費雪訊息矩陣, 進而得到系統可靠度之相關推論。;High reliability products have longer lifetime under normal environment. Accelerated life tests are
    usually used to reduce the experiment time. In a series system, the system fails when any of the
    components fails, while the cause of system failure may not be observed which is known as masked
    data. In this thesis, we consider the step-stress accelerated life tests for series systems with Type-I
    censoring, in which the lifetimes of components are exponentially distributed. We not only consider
    those distributions are independent, but also consider the Marshall-Olkin bivariate distribution for
    two-components series systems. Assume that there exists log-linear relationship between the mean
    lifetime of components and the levels of the environmental stress variables under the cumulative exposure model, and the data analyzed are interval data in the sense only the numbers of failures are observed at the times of changing stress levels. Maximum likelihood inference is developed
    incorporated with the EM algorithm as well as the missing information principle to achieving the Fisher information. Simulation study is carried out in the reliability analysis. It shows that the proposed method is more accurate and efficient than the bootstrap method.
    Appears in Collections:[統計研究所] 博碩士論文

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