本文選題：剩余壽命預(yù)測 + 不確定性��； 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：預(yù)測與健康管理技術(shù)(Prognostics and Health Management,PHM)是一種新型的裝備綜合保障技術(shù),可以大大提高系統(tǒng)設(shè)備的可靠性和安全性,還能降低復(fù)雜系統(tǒng)設(shè)備的維護(hù)費(fèi)用。預(yù)測與健康管理技術(shù)包括故障預(yù)測(Prognostics)和健康管理(Management)兩個部分。其中,所謂故障預(yù)測就是根據(jù)系統(tǒng)現(xiàn)在或者歷史的性能狀態(tài)預(yù)測部件未來的健康狀態(tài),比如,確定設(shè)備的剩余壽命或者正常工作的時(shí)間長度。因此,剩余壽命(Remaining Useful Life, RUL)預(yù)測技術(shù)是預(yù)測與健康管理技術(shù)中一項(xiàng)核心問題。準(zhǔn)確預(yù)測隨機(jī)退化產(chǎn)品的剩余壽命是進(jìn)行預(yù)測與健康管理的基礎(chǔ),也是工程實(shí)踐中的重難點(diǎn)問題。對于此類復(fù)雜產(chǎn)品,隨機(jī)多變的退化演變規(guī)律一般難以機(jī)理建模,而傳統(tǒng)的基于壽命數(shù)據(jù)的方法對于小樣本、高成本的設(shè)備則難以實(shí)施。因此,基于狀態(tài)監(jiān)測數(shù)據(jù)進(jìn)行退化建模和剩余壽命預(yù)測,進(jìn)而實(shí)現(xiàn)管理決策的技術(shù)成為了當(dāng)前可靠性工程領(lǐng)域的研究前沿。本文主要基于退化數(shù)據(jù)的可靠性建模對剩余壽命預(yù)測方法進(jìn)行了以下兩個方面的研究:1、針對線性模型,本文在首達(dá)時(shí)間的概念下,提出一種同時(shí)考慮參數(shù)不確定性和測量不確定性的Wiener過程退化模型,并推導(dǎo)了考慮了含參數(shù)噪聲和測量誤差的wiener退化設(shè)備剩余壽命概率密度函數(shù)的解析解。同時(shí)實(shí)現(xiàn)了在線剩余壽命預(yù)測。并用蒙特卡洛仿真驗(yàn)證了本文方法的有效性,最后激光管的實(shí)驗(yàn)結(jié)果表明本文提出的方法能顯著提高剩余壽命預(yù)測的精度。2、針對非線性模型,本文基于馬里蘭大學(xué)鋰離子電池循環(huán)壽命退化數(shù)據(jù),對鋰離子電池的壽命退化過程進(jìn)行分析并選擇經(jīng)驗(yàn)退化模型,提出一種基于EKF/KF算法的離子電池剩余壽命預(yù)測方法。使用EKF算法對歷史數(shù)據(jù)進(jìn)行參數(shù)估計(jì),然后利用估計(jì)的參數(shù)基于KF算法來對鋰離子電池剩余壽命進(jìn)行估計(jì),利用馬里蘭大學(xué)的鋰離子電池?cái)?shù)據(jù)驗(yàn)證算法的有效性,并用MAE指標(biāo)對算法進(jìn)行評價(jià)。
[Abstract]:Prognostics and Health Management is a new integrated equipment support technology, which can greatly improve the reliability and security of system equipment and reduce the maintenance cost of complex system equipment. Prediction and health management technology include two parts: fault prediction (Prognostics) and health management (management). The so-called fault prediction is to predict the future health state of the components according to the current or historical performance of the system, for example, to determine the remaining life of the equipment or the length of the normal working time. Therefore, residual life Useful Life, RUL) prediction technology is a core problem in prediction and health management technology. Accurate prediction of residual life of randomly degraded products is the basis of prediction and health management, and is also a difficult problem in engineering practice. For this kind of complex products, it is difficult to model the mechanism of random and changeable degradation evolution law, but the traditional method based on life data is difficult to implement for small sample and high cost equipment. Therefore, the technology of modeling degradation and predicting residual life based on state monitoring data has become the research frontier in the field of reliability engineering. In this paper, based on the reliability modeling of degenerate data, the residual life prediction method is studied in the following two aspects: 1. For the linear model, this paper is based on the concept of first arrival time. A degenerate model of Wiener process considering both parameter uncertainty and measurement uncertainty is proposed, and the analytical solution of residual life probability density function of wiener degenerate equipment with parameter noise and measurement error is derived. At the same time, the online residual life prediction is realized. The effectiveness of the proposed method is verified by Monte Carlo simulation. The experimental results of the laser tube show that the proposed method can significantly improve the accuracy of residual life prediction. Based on the cyclic life degradation data of Li-ion batteries at the University of Maryland, this paper analyzes the degradation process of Li-ion batteries and selects an empirical degradation model. A method for predicting the residual life of Li-ion batteries based on EKF/KF algorithm is proposed. The EKF algorithm is used to estimate the parameters of the historical data, then the estimated parameters are used to estimate the residual life of the lithium ion battery based on KF algorithm. The validity of the algorithm is verified by the lithium ion battery data of the University of Maryland. The algorithm is evaluated with MAE index.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912;O213.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 鄭建飛;胡昌華;司小勝;張正新;張鑫;;考慮不確定測量和個體差異的非線性隨機(jī)退化系統(tǒng)剩余壽命估計(jì)[J];自動化學(xué)報(bào);2017年02期

2 司小勝;胡昌華;周東華;;帶測量誤差的非線性退化過程建模與剩余壽命估計(jì)[J];自動化學(xué)報(bào);2013年05期

3 彭寶華;周經(jīng)倫;馮靜;劉學(xué)敏;;金屬化膜脈沖電容器剩余壽命預(yù)測方法研究[J];電子學(xué)報(bào);2011年11期

4 孟光;尤明懿;;基于狀態(tài)監(jiān)測的設(shè)備壽命預(yù)測與預(yù)防維護(hù)規(guī)劃研究進(jìn)展[J];振動與沖擊;2011年08期

5 彭寶華;周經(jīng)倫;潘正強(qiáng);;Wiener過程性能退化產(chǎn)品可靠性評估的Bayes方法[J];系統(tǒng)工程理論與實(shí)踐;2010年03期

6 趙建印;劉芳;孫權(quán);周經(jīng)倫;;金屬化膜脈沖電容器在線可靠性評估與性能預(yù)計(jì)[J];兵工學(xué)報(bào);2006年02期

相關(guān)博士學(xué)位論文 前1條

1 尤明懿;基于狀態(tài)監(jiān)測數(shù)據(jù)的產(chǎn)品壽命預(yù)測與預(yù)測維護(hù)規(guī)劃方法研究[D];上海交通大學(xué);2012年

，

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