基于貝葉斯信息融合的復雜系統(tǒng)可靠性增長分階段評價方法
發(fā)布時間：2019-03-17 12:19
【摘要】：大型復雜系統(tǒng)的質量關系到地區(qū)或國家在某一個大型項目上的成敗,甚至是在某一個領域的國際地位,同時關系到生命財產(chǎn)安全。可靠性是質量的固有屬性之一,可靠性貫穿產(chǎn)品或系統(tǒng)的研制、定型到投入使用的整個過程,因此必須重視可靠性管理。研制階段通過可靠性增長試驗實現(xiàn)可靠性增長,進行可靠性評估時常用的有Duane模型,AMSAA模型和Bayes可靠性評估方法等。大型復雜系統(tǒng),有成千上萬的不同類型的器件組成,本文研究了復雜系統(tǒng)增長試驗連續(xù)進行,相似產(chǎn)品較少,系統(tǒng)的失效機理也很難掌握情況下,在可靠性增長過程中出現(xiàn)突變點時,突變點的辨識和系統(tǒng)可靠性增長評估問題,重點對突變點導致的增長速度減緩情況開展研究。通過增長趨勢圖建立分段模型辨識突變點,在此基礎上,基于最大熵方法確定Bayes先驗分布,通過某大型裝置安裝集成階段的數(shù)據(jù)進行驗證,證明了方法的有效性和可用性。首先介紹了研究背景,研究目的和意義等內(nèi)容。在第二章介紹了可靠性的相關概念和指標,以及可靠性增長管理的模型和方法的綜述。第三章分析了可靠性增長突變的原因,建立了基于增長趨勢的可靠性增長分段模型;能夠體現(xiàn)糾正措施對增長特性的影響,具有較為廣泛的應用范圍。第四章在多階段系統(tǒng)可靠性增長評估研究的基礎上,建立了基于最大熵方法的Bayes可靠性評估模型;通過案例證明了模型的有效性。通過分析認為,辨識突變點有利于本文研究的開展;最大熵方法在進行Bayes模型的先驗參數(shù)求解時的有效性和方便性;Bayes模型能夠有效對多階段數(shù)據(jù)信息進行融合。本文主要得到如下結論:(1)建立的分段模型適用于突變點導致的可靠性增長速度加快,減緩和多突變點下的增長速度的不確定變化。(2)建立的分段模型能更明確可靠性增長速度變化特點,可以更好地了解系統(tǒng)可靠性增長的變化規(guī)律;(3)建立的系統(tǒng)可靠性增長評估方法用于融合多階段的故障信息,得到更準確的評估結果。
[Abstract]:The quality of large-scale complex system is related to the success or failure of a region or country in a large-scale project, even to the international status in a certain field, and also to the safety of life and property. Reliability is one of the inherent attributes of quality. Reliability runs through the whole process of product or system development, setting up to put into use, so it is necessary to pay attention to reliability management. In the development stage, reliability growth is realized by reliability growth test. Duane model, AMSAA model and Bayes reliability evaluation method are commonly used in reliability evaluation. There are thousands of different types of devices in large-scale complex systems. In this paper, the growth tests of complex systems are carried out continuously, the similar products are few, and the failure mechanism of the system is difficult to grasp. In the process of reliability growth, the identification of catastrophe points and the evaluation of system reliability growth are discussed, and the research on the deceleration of the growth rate caused by the mutation points is emphasized. A piecewise model based on the growth trend graph is used to identify the mutation points. On the basis of this, the prior distribution of Bayes is determined based on the maximum entropy method. The validity and usability of the method are verified by the data of the installation and integration stage of a large device. Firstly, the background, purpose and significance of the research are introduced. In the second chapter, the related concepts and indicators of reliability are introduced, and the models and methods of reliability growth management are summarized. In the third chapter, the reason of the sudden change of reliability growth is analyzed, and the subsection model of reliability growth based on growth trend is established, which can reflect the influence of corrective measures on the growth characteristics and has a wide range of applications. In chapter 4, based on the research of multi-stage system reliability growth evaluation, the Bayes reliability evaluation model based on the maximum entropy method is established, and the validity of the model is proved by a case. Through the analysis, it is concluded that identifying the mutation points is beneficial to the research in this paper; the maximum entropy method is effective and convenient in solving the prior parameters of the Bayes model; and the Bayes model can effectively fuse the multi-stage data information. The main conclusions of this paper are as follows: (1) the proposed piecewise model is suitable for the acceleration of reliability growth caused by mutation points. (2) the piecewise model can clarify the change characteristics of reliability growth rate more clearly, and can better understand the change rule of system reliability growth rate; (2) the variable law of system reliability growth can be better understood by the piecewise model which can alleviate the uncertain change of growth speed under multi-mutation points; (3) the proposed reliability growth assessment method is used to fuse multi-stage fault information and obtain more accurate evaluation results.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F124;F224
本文編號：2442295
[Abstract]:The quality of large-scale complex system is related to the success or failure of a region or country in a large-scale project, even to the international status in a certain field, and also to the safety of life and property. Reliability is one of the inherent attributes of quality. Reliability runs through the whole process of product or system development, setting up to put into use, so it is necessary to pay attention to reliability management. In the development stage, reliability growth is realized by reliability growth test. Duane model, AMSAA model and Bayes reliability evaluation method are commonly used in reliability evaluation. There are thousands of different types of devices in large-scale complex systems. In this paper, the growth tests of complex systems are carried out continuously, the similar products are few, and the failure mechanism of the system is difficult to grasp. In the process of reliability growth, the identification of catastrophe points and the evaluation of system reliability growth are discussed, and the research on the deceleration of the growth rate caused by the mutation points is emphasized. A piecewise model based on the growth trend graph is used to identify the mutation points. On the basis of this, the prior distribution of Bayes is determined based on the maximum entropy method. The validity and usability of the method are verified by the data of the installation and integration stage of a large device. Firstly, the background, purpose and significance of the research are introduced. In the second chapter, the related concepts and indicators of reliability are introduced, and the models and methods of reliability growth management are summarized. In the third chapter, the reason of the sudden change of reliability growth is analyzed, and the subsection model of reliability growth based on growth trend is established, which can reflect the influence of corrective measures on the growth characteristics and has a wide range of applications. In chapter 4, based on the research of multi-stage system reliability growth evaluation, the Bayes reliability evaluation model based on the maximum entropy method is established, and the validity of the model is proved by a case. Through the analysis, it is concluded that identifying the mutation points is beneficial to the research in this paper; the maximum entropy method is effective and convenient in solving the prior parameters of the Bayes model; and the Bayes model can effectively fuse the multi-stage data information. The main conclusions of this paper are as follows: (1) the proposed piecewise model is suitable for the acceleration of reliability growth caused by mutation points. (2) the piecewise model can clarify the change characteristics of reliability growth rate more clearly, and can better understand the change rule of system reliability growth rate; (2) the variable law of system reliability growth can be better understood by the piecewise model which can alleviate the uncertain change of growth speed under multi-mutation points; (3) the proposed reliability growth assessment method is used to fuse multi-stage fault information and obtain more accurate evaluation results.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F124;F224
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，本文編號：2442295
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