【摘要】：故障診斷主要研究如何對(duì)系統(tǒng)中出現(xiàn)的故障進(jìn)行檢測(cè)、分離和辨識(shí),即判斷故障是否發(fā)生,定位故障發(fā)生的部位和種類以及確定故障發(fā)生的時(shí)間和故障發(fā)生的大小。動(dòng)態(tài)因果圖基于一些明顯特點(diǎn)(布爾邏輯運(yùn)算的引入、完全基于概率論等),和有關(guān)因果圖推理的算法研究的不斷優(yōu)化,使其在系統(tǒng)的故障診斷中應(yīng)用得越來(lái)越廣泛。針對(duì)現(xiàn)實(shí)應(yīng)用中存在的各種不確定性因素,本論文以因果圖理論為基礎(chǔ),結(jié)合灰色系統(tǒng)理論的優(yōu)點(diǎn),將其應(yīng)于復(fù)雜系統(tǒng)的故障診斷中,提高故障診斷的效率。主要內(nèi)容包括:(1)按預(yù)測(cè)、分析、提出預(yù)防重點(diǎn)的分析步驟,從整體上對(duì)系統(tǒng)進(jìn)行故障診斷分析。首先用灰色災(zāi)變預(yù)測(cè)模型對(duì)事故多發(fā)年份進(jìn)行預(yù)測(cè),且模型的預(yù)測(cè)精度較高。其次對(duì)系統(tǒng)的檢修可以根據(jù)因果圖故障分析中的基本事件重要度分析方法得到,選擇重要度大的部件進(jìn)行檢修。但是傳統(tǒng)的基本事件重要度有3種,本文考慮通過(guò)引入灰色關(guān)聯(lián)理論,得到更為直觀的基本事件排序,實(shí)例表明此方法的引入是合理的。最后提出預(yù)防重點(diǎn),制定合理有效的故障應(yīng)對(duì)方案,以減少此類事故的發(fā)生。(2)因果圖的一個(gè)最小割集代表一種故障模式,基于因果圖故障模式的診斷通常是根據(jù)其重要度大小來(lái)排查故障原因。在本文中,用模糊數(shù)來(lái)刻畫(huà)事件發(fā)生的概率,根據(jù)因果圖基本事件的模糊重要度,將灰色關(guān)聯(lián)分析引入,對(duì)各種故障模式發(fā)生的可能性大小作出判斷,據(jù)此可以縮小故障診斷空間。同時(shí)該方法也解決了由于故障信息缺乏所導(dǎo)致的中間事件和基本事件的概率無(wú)法確知,以及基本事件與中間事件的相互關(guān)系難以確定的問(wèn)題。
[Abstract]:Fault diagnosis mainly studies how to detect, separate and identify the faults in the system, that is to say, to judge whether the faults occur, to locate the locations and types of the faults, and to determine the time and magnitude of the faults. Dynamic causality diagram is based on some obvious features (Boolean logic operation, probability theory, etc.), and the continuous optimization of reasoning algorithms about causal diagram makes it more and more widely used in system fault diagnosis. Based on causality diagram theory and the advantages of grey system theory, this paper aims at the uncertain factors existing in practical application, and puts it into the fault diagnosis of complex system to improve the efficiency of fault diagnosis. The main contents are as follows: (1) according to the prediction and analysis, the key steps of prevention are put forward, and the fault diagnosis and analysis of the system as a whole is carried out. First, the grey disaster prediction model is used to predict the year of frequent accidents, and the prediction accuracy of the model is high. Secondly, the maintenance of the system can be obtained according to the basic event importance analysis method in the causality diagram fault analysis, and the parts with the high importance can be selected for overhaul. But there are three kinds of traditional basic event importance. This paper considers introducing grey relation theory to get a more intuitionistic sort of basic event. The example shows that this method is reasonable. Finally, the prevention focus is put forward, and a reasonable and effective fault response scheme is made to reduce the occurrence of such accidents. (2) A minimal cut set of causality diagram represents a fault mode. Fault diagnosis based on causality diagram is usually based on its importance to detect the cause of failure. In this paper, fuzzy numbers are used to describe the probability of occurrence of events. According to the fuzzy importance of the basic events in causality diagram, grey relational analysis is introduced to judge the probability of occurrence of various fault modes. Therefore, the fault diagnosis space can be reduced. The method also solves the problem that the probability of intermediate events and basic events can not be ascertained due to the lack of fault information, and the relationship between basic events and intermediate events is difficult to determine.
【學(xué)位授予單位】：重慶師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：N941.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 彭霜霜;王洪春;;基于因果圖最小割集和最小徑集在故障系統(tǒng)中的診斷[J];南京師范大學(xué)學(xué)報(bào)(工程技術(shù)版);2015年03期

2 曲彥光;張勤;朱群雄;;動(dòng)態(tài)不確定因果圖在化工系統(tǒng)動(dòng)態(tài)故障診斷中的應(yīng)用[J];智能系統(tǒng)學(xué)報(bào);2015年03期

3 趙越;張勤;鄧宏琛;董春玲;;DUCG在核電站二回路故障診斷中的應(yīng)用[J];原子能科學(xué)技術(shù);2014年S1期

4 董春玲;張勤;;用于不確定性故障診斷的權(quán)重邏輯推理算法研究[J];自動(dòng)化學(xué)報(bào);2014年12期

5 Chun-Ling Dong;Qin Zhang;Shi-Chao Geng;;A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis[J];International Journal of Automation & Computing;2014年03期

6 楊佳婧;張勤;朱群雄;;動(dòng)態(tài)不確定因果圖在化工過(guò)程故障診斷中的應(yīng)用[J];智能系統(tǒng)學(xué)報(bào);2014年02期

7 趙紅言;張君;張建強(qiáng);;灰色關(guān)聯(lián)分析法在電子設(shè)備故障樹(shù)中的應(yīng)用[J];實(shí)驗(yàn)室研究與探索;2014年03期

8 黎奇志;胡國(guó)平;趙紅言;;加權(quán)灰色關(guān)聯(lián)分析在故障診斷中的應(yīng)用研究[J];微計(jì)算機(jī)信息;2012年07期

9 丁水汀;鮑夢(mèng)瑤;;基于因果圖的微小型渦噴發(fā)動(dòng)機(jī)安全性分析(英文)[J];Transactions of Nanjing University of Aeronautics & Astronautics;2011年03期

10 姚成玉;張熒驛;王旭峰;陳東寧;;T-S模糊故障樹(shù)重要度分析方法[J];中國(guó)機(jī)械工程;2011年11期

相關(guān)碩士學(xué)位論文 前7條

1 梁帆;因果圖推理的改進(jìn)及應(yīng)用[D];重慶師范大學(xué);2016年

2 張俊;基于灰色理論的變壓器故障預(yù)測(cè)與評(píng)估[D];西華大學(xué);2012年

3 李國(guó)志;基于因果圖的灰色馬氏鏈及其在智能信號(hào)控制燈中的應(yīng)用研究[D];重慶師范大學(xué);2010年

4 鄭佩;基于案例推理的故障診斷技術(shù)研究[D];華中科技大學(xué);2008年

5 鄭蕊蕊;基于灰色系統(tǒng)理論的電力變壓器故障診斷技術(shù)[D];吉林大學(xué);2007年

6 李波;動(dòng)態(tài)因果圖用于汽油發(fā)動(dòng)機(jī)故障智能診斷的理論及方法研究[D];重慶大學(xué);2002年

7 王宏川;因果圖推理及其應(yīng)用研究[D];重慶大學(xué);2002年

，

本文編號(hào)：2143693