【摘要】：大型復(fù)雜系統(tǒng)的質(zhì)量關(guān)系到地區(qū)或國(guó)家在某一個(gè)大型項(xiàng)目上的成敗,甚至是在某一個(gè)領(lǐng)域的國(guó)際地位,同時(shí)關(guān)系到生命財(cái)產(chǎn)安全�？煽啃允琴|(zhì)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(gè)過(guò)程,因此必須重視可靠性管理。研制階段通過(guò)可靠性增長(zhǎng)試驗(yàn)實(shí)現(xiàn)可靠性增長(zhǎng),進(jìn)行可靠性評(píng)估時(shí)常用的有Duane模型,AMSAA模型和Bayes可靠性評(píng)估方法等。大型復(fù)雜系統(tǒng),有成千上萬(wàn)的不同類(lèi)型的器件組成,本文研究了復(fù)雜系統(tǒng)增長(zhǎng)試驗(yàn)連續(xù)進(jìn)行,相似產(chǎn)品較少,系統(tǒng)的失效機(jī)理也很難掌握情況下,在可靠性增長(zhǎng)過(guò)程中出現(xiàn)突變點(diǎn)時(shí),突變點(diǎn)的辨識(shí)和系統(tǒng)可靠性增長(zhǎng)評(píng)估問(wèn)題,重點(diǎn)對(duì)突變點(diǎn)導(dǎo)致的增長(zhǎng)速度減緩情況開(kāi)展研究。通過(guò)增長(zhǎng)趨勢(shì)圖建立分段模型辨識(shí)突變點(diǎn),在此基礎(chǔ)上,基于最大熵方法確定Bayes先驗(yàn)分布,通過(guò)某大型裝置安裝集成階段的數(shù)據(jù)進(jìn)行驗(yàn)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(guān)概念和指標(biāo),以及可靠性增長(zhǎng)管理的模型和方法的綜述。第三章分析了可靠性增長(zhǎng)突變的原因,建立了基于增長(zhǎng)趨勢(shì)的可靠性增長(zhǎng)分段模型;能夠體現(xiàn)糾正措施對(duì)增長(zhǎng)特性的影響,具有較為廣泛的應(yīng)用范圍。第四章在多階段系統(tǒng)可靠性增長(zhǎng)評(píng)估研究的基礎(chǔ)上,建立了基于最大熵方法的Bayes可靠性評(píng)估模型;通過(guò)案例證明了模型的有效性。通過(guò)分析認(rèn)為,辨識(shí)突變點(diǎn)有利于本文研究的開(kāi)展;最大熵方法在進(jìn)行Bayes模型的先驗(yàn)參數(shù)求解時(shí)的有效性和方便性;Bayes模型能夠有效對(duì)多階段數(shù)據(jù)信息進(jìn)行融合。本文主要得到如下結(jié)論:(1)建立的分段模型適用于突變點(diǎn)導(dǎo)致的可靠性增長(zhǎng)速度加快,減緩和多突變點(diǎn)下的增長(zhǎng)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(zhǎng)速度變化特點(diǎn),可以更好地了解系統(tǒng)可靠性增長(zhǎng)的變化規(guī)律;(3)建立的系統(tǒng)可靠性增長(zhǎng)評(píng)估方法用于融合多階段的故障信息,得到更準(zhǔn)確的評(píng)估結(jié)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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F124;F224

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本文編號(hào)：2442295