【摘要】：在實(shí)際工程應(yīng)用中,系統(tǒng)或設(shè)備可能會(huì)發(fā)生各種各樣的故障,需要進(jìn)行視情維修(Condition Based Maintenance,CBM)。而剩余壽命預(yù)測(cè)(Remaining Life,RL)是視情維修過(guò)程中的核心問(wèn)題之一;另一方面,在廢舊產(chǎn)品回收利用和再制造過(guò)程中通過(guò)剩余壽命預(yù)測(cè)才能判斷回收部件是否值得再制造,對(duì)再制造部件也需要預(yù)測(cè)其剩余壽命從而確定其價(jià)值。因此設(shè)備或系統(tǒng)的剩余壽命預(yù)測(cè)具有重大的研究意義和實(shí)用價(jià)值。首先,本文綜述了系統(tǒng)或設(shè)備剩余壽命預(yù)測(cè)的兩種方法:基于系統(tǒng)或設(shè)備損傷機(jī)理的模型方法和基于數(shù)據(jù)的統(tǒng)計(jì)分析方法,針對(duì)數(shù)據(jù)處理的建模過(guò)程,介紹了基于維納過(guò)程,馬爾科夫鏈,泊松過(guò)程的方法;然后綜述了相應(yīng)的參數(shù)估計(jì)方法:極大似然估計(jì)方法(MLE),期望最大化算法(EM)和貝葉斯方法。其次,針對(duì)沖擊載荷對(duì)降級(jí)系統(tǒng)的影響分別建立了三種模型:靜態(tài)沖擊退化模型,累積沖擊退化模型,極端沖擊退化模型。并推導(dǎo)出這三種模型的剩余壽命概率密度函數(shù),數(shù)值試驗(yàn)結(jié)果表明考慮沖擊載荷進(jìn)行剩余壽命預(yù)測(cè)提高了預(yù)測(cè)的精度。接著針對(duì)具有工作狀態(tài)和存儲(chǔ)狀態(tài)的降級(jí)系統(tǒng),采用極大似然估計(jì)法和EM算法估計(jì)了模型中的未知參數(shù),并使用蒙特卡羅方法預(yù)測(cè)剩余壽命,從而推導(dǎo)出剩余壽命分布函數(shù)。最后對(duì)相關(guān)數(shù)據(jù)進(jìn)行對(duì)比和分析,驗(yàn)證了所提方法的有效性。最后對(duì)本文所提出的方法進(jìn)行了總結(jié)和展望。由于缺少相應(yīng)的實(shí)證實(shí)驗(yàn),很難獲得足夠的相關(guān)數(shù)據(jù)和定量信息,因此本文提出的兩種方法的實(shí)際應(yīng)用效果還需要進(jìn)一步驗(yàn)證。另外利用連續(xù)時(shí)間馬爾科夫鏈近似描述系統(tǒng)工作狀態(tài)和存儲(chǔ)狀態(tài)的降級(jí)過(guò)程條件過(guò)于苛刻,在這方面還需要進(jìn)一步研究和發(fā)展。
[Abstract]:In practical engineering applications, the system or equipment may occur a variety of failures, the need for situational maintenance (Condition Based Maintenance,CBM). Residual life prediction (Remaining Life,RL) is one of the core problems in the process of situational maintenance. On the other hand, in the process of recycling and remanufacturing of waste products, the residual life prediction can be used to determine whether the recovered parts are worth remanufacturing or not, and it is also necessary to predict the residual life of the remanufactured parts to determine their value. Therefore, the residual life prediction of equipment or system has great significance and practical value. Firstly, this paper summarizes two methods for predicting the residual life of system or equipment: the model method based on the damage mechanism of system or equipment and the statistical analysis method based on data. In view of the modeling process of data processing, the Wiener process is introduced. Markov chain, Poisson process method; Then the corresponding parameter estimation methods are summarized: maximum likelihood estimation (MLE), expectation maximization algorithm (EM) and Bayesian method (Bayesian method). Secondly, three models are established for the impact of impact load on the degradation system: static impact degradation model, cumulative impact degradation model and extreme impact degradation model. The probability density function of residual life of these three models is derived. The numerical results show that the prediction accuracy of residual life is improved by considering the impact load. Then, for the degraded system with working state and storage state, the unknown parameters in the model are estimated by maximum likelihood estimation and EM algorithm, and the residual life is predicted by Monte Carlo method, and the residual life distribution function is deduced. Finally, the validity of the proposed method is verified by comparing and analyzing the relevant data. Finally, the methods proposed in this paper are summarized and prospected. Due to the lack of corresponding empirical experiments, it is difficult to obtain enough relevant data and quantitative information. Therefore, the practical application results of the two methods proposed in this paper need to be further verified. In addition, it is too harsh to use continuous time Markov chain to approximate describe the working state and storage state of the system, which needs further research and development.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：O213.2

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