具有韋伯壽命零件的串聯系統之可靠度分析; Reliability Analysis of a Series System with Weibull Lifetime Components

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7745

Title:	具有韋伯壽命零件的串聯系統之可靠度分析;Reliability Analysis of a Series System with Weibull Lifetime Components
Authors:	黃偉恆;Wei-heng Huang
Contributors:	統計研究所
Keywords:	EM 演算法;有母數拔靴法;type-I 設限;貝氏估計;串聯系統;韋伯分佈;最大概似估計量;可靠度函數。;parametric bootstrap method;EM-algorithm;reliability function.;maximum likelihood estimators;Weibull distribution;Series system;Type-I censoring;Bayesian estimators
Date:	2009-06-10
Issue Date:	2009-09-22 11:03:51 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	在串聯系統中，只要有一個零件失效，系統即無法運作。然而每個零件可能有不同的失效時間分佈，使得系統停擺的時間往往是不確定的。本文考慮具有m個零件的串聯系統，假設每個零件之壽命具韋伯分佈且彼此獨立，若只觀察到系統失效之時間時之可靠度分析。首先，以EM演算法求得各零件壽命分佈中參數的最大概似估計量及相關統計推論；另外並考慮以無資訊先驗分佈的 "馬可夫鏈蒙地卡羅"方法之客觀貝氏推論。　更進一步地將上述方法，發展於 type-I 設限實驗中，另以有母數的拔靴法以估計該模型下參數最大概似估計量的標準差。模擬結果顯示，在兩種實驗中，最大概似推論與貝氏推論都能提供準確的估計，而樣本不是太大時，無資訊先驗分佈之貝氏分析所得結果較最大概似法為佳。 A series system fails if any of its components fails. However, each component may have different life time distribution and, in practice, the exact component responsible for the failure of the system can not often be identified. This paper considers a life test on a series system of m components, each having a Weibull life time distribution, and when only the system failure time is observed. The maximum likelihood estimates via EM algorithm is developed for the parameters of each component as well as for the system reliability. Objective Bayesian inference incorporated with the Markov chain Monte Carlo method is also addressed. Furthermore, statistical inference is developed for the Type-I censoring experiment within a prespecified time interval and parametric bootstrap method is used to estimate the standard errors of the MLE in this case. Simulation study carried out reveals that the Bayesian analysis with noninformative prior provides better results than the likelihood approach in both situations at least in the case of small sample sizes.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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