本文選題：系統(tǒng)可靠性分析 + 動(dòng)態(tài)故障樹分析　； 參考：《電子科技大學(xué)》2013年博士論文

【摘要】：隨著現(xiàn)代工程系統(tǒng)的大型化、復(fù)雜化以及高新技術(shù)的引入，系統(tǒng)可靠性已經(jīng)成為制約復(fù)雜系統(tǒng)發(fā)展的關(guān)鍵所在�？煽啃苑治黾夹g(shù)作為實(shí)施系統(tǒng)可靠性工程的關(guān)鍵基礎(chǔ)技術(shù)，目前正面臨著復(fù)雜系統(tǒng)所帶來的若干技術(shù)難點(diǎn)和應(yīng)用挑戰(zhàn)。針對(duì)復(fù)雜系統(tǒng)的可靠性分析技術(shù)已經(jīng)成為可靠性工程領(lǐng)域的研究熱點(diǎn)及難點(diǎn)問題之一。系統(tǒng)可靠性分析常規(guī)方法主要包括：可靠性框圖法、故障模式影響及危害性分析法、故障樹分析法、Petri網(wǎng)方法以及蒙特卡洛數(shù)值仿真方法等。常規(guī)方法通常不考慮系統(tǒng)的動(dòng)態(tài)失效特性，且多數(shù)建立在零部件故障相互獨(dú)立和故障數(shù)據(jù)完備的基礎(chǔ)之上。在實(shí)際復(fù)雜工程系統(tǒng)中，零部件失效之間通常并不是相互獨(dú)立的，往往存在著多種復(fù)雜的關(guān)聯(lián)關(guān)系和動(dòng)態(tài)特性，比如部件失效的順序關(guān)系。另一方面，由于成本、時(shí)間、管理和人因等多方面的原因?qū)е铝悴考?shù)據(jù)存在模糊不確定性。目前，在考慮動(dòng)態(tài)失效特性的故障樹分析方面已取得了一定成果。然而，在同時(shí)考慮模糊不確定性以及動(dòng)態(tài)失效特性等情況下的故障樹分析方面的研究工作還很缺乏，以致用常規(guī)方法分析所得結(jié)果與實(shí)際情況不符甚至相差甚遠(yuǎn)。因此，迫切需要開展考慮零部件動(dòng)態(tài)失效特性和模糊不確定性的系統(tǒng)可靠性分析方法的研究。 針對(duì)上述問題，本文主要開展了以下研究工作： （1）基于模糊馬爾科夫模型的動(dòng)態(tài)故障樹分析方法。馬爾科夫模型方法是一種狀態(tài)空間分析方法，用該模型能夠準(zhǔn)確地描述失效分布與維修分布都服從指數(shù)分布的系統(tǒng)的失效及維修過程。本文在基于馬爾科夫模型的基礎(chǔ)上，考慮了零部件失效信息的模糊不確定性，研究了在模糊失效率下的動(dòng)態(tài)故障樹分析方法。通過建立系統(tǒng)的動(dòng)態(tài)故障樹模型，并運(yùn)用三角模糊數(shù)來描述零部件和系統(tǒng)的失效率，通過已經(jīng)得到的動(dòng)態(tài)故障樹模型建立系統(tǒng)失效過程的模糊馬爾科夫模型。運(yùn)用模糊理論中擴(kuò)展原理的思想和Laplace-Stieltjes變換求解該模型，得到系統(tǒng)在給定時(shí)刻下的模糊失效概率和給定隸屬度下的模糊可靠度曲線。最后應(yīng)用該模糊馬爾科夫模型對(duì)某數(shù)控加工中心液壓系統(tǒng)進(jìn)行可靠性建模與分析。研究結(jié)果表明，該方法能夠有效地對(duì)具有動(dòng)態(tài)失效特性和模糊不確定性的系統(tǒng)進(jìn)行可靠性建模及定量評(píng)估。 （2）基于離散時(shí)間貝葉斯網(wǎng)絡(luò)的動(dòng)態(tài)故障樹可靠性評(píng)估模型。研究了基于貝葉斯網(wǎng)絡(luò)和動(dòng)態(tài)故障樹的系統(tǒng)可靠性建模和評(píng)估方法。通過把系統(tǒng)失效的動(dòng)態(tài)故障樹模型轉(zhuǎn)化為貝葉斯網(wǎng)絡(luò)模型，，并運(yùn)用貝葉斯網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)來表達(dá)系統(tǒng)中部件失效之間的邏輯關(guān)系。針對(duì)基于馬爾科夫模型的動(dòng)態(tài)故障樹求解方法中存在的狀態(tài)爆炸問題，借助貝葉斯網(wǎng)絡(luò)的條件獨(dú)立性來降低模型求解的復(fù)雜度。在此基礎(chǔ)上，建立了靜態(tài)和動(dòng)態(tài)故障樹中各種邏輯門的條件概率分布的公式，以實(shí)現(xiàn)對(duì)系統(tǒng)失效過程及其動(dòng)態(tài)特性進(jìn)行建模和分析。以衛(wèi)星太陽翼驅(qū)動(dòng)機(jī)構(gòu)為對(duì)象，建立了動(dòng)態(tài)故障樹模型和相應(yīng)的貝葉斯網(wǎng)絡(luò)模型，并運(yùn)用聯(lián)合樹推理算法對(duì)該模型進(jìn)行了雙向概率推理。實(shí)例分析結(jié)果表明：該方法能夠有效地解決具有動(dòng)態(tài)失效特性的復(fù)雜系統(tǒng)的可靠性分析和評(píng)估問題。 （3）模糊數(shù)據(jù)下基于連續(xù)時(shí)間貝葉斯網(wǎng)絡(luò)的動(dòng)態(tài)故障樹分析方法。研究了考慮模糊不確定性的基于連續(xù)時(shí)間貝葉斯網(wǎng)絡(luò)的系統(tǒng)可靠性建模與分析方法�；谶B續(xù)時(shí)間貝葉斯網(wǎng)絡(luò)模型的方法能夠直接得到系統(tǒng)的可靠度和失效概率的解析表達(dá)式。本文用三角模糊數(shù)描述零部件的失效率，并用其來構(gòu)造零部件的模糊邊緣失效密度函數(shù)及模糊失效分布函數(shù)。用單位階躍函數(shù)和沖激函數(shù)來構(gòu)造貝葉斯網(wǎng)絡(luò)中非根節(jié)點(diǎn)失效事件的條件概率密度函數(shù)和分布函數(shù)。在此基礎(chǔ)上，推導(dǎo)了在模糊失效率下的幾種典型的故障樹邏輯門輸出事件發(fā)生的模糊邊緣失效密度函數(shù)和模糊失效分布函數(shù)的表達(dá)式。最后，運(yùn)用算例驗(yàn)證了該方法的正確性和有效性，并通過對(duì)大型礦用挖掘機(jī)電氣系統(tǒng)整流回饋?zhàn)酉到y(tǒng)的建模與分析闡述了該方法在實(shí)際工程系統(tǒng)中的應(yīng)用。 （4）考慮共因失效的動(dòng)態(tài)故障樹分析方法。運(yùn)用故障樹分析方法對(duì)具有共因失效的系統(tǒng)進(jìn)行了可靠性分析。闡述了當(dāng)前共因失效研究中的一些經(jīng)典模型和建模方法，運(yùn)用顯式建模方法與平方根模型對(duì)某動(dòng)車組追尾事故進(jìn)行了故障樹分析。分別計(jì)算了考慮共因失效和假設(shè)部件失效獨(dú)立兩種情況下的系統(tǒng)失效概率。結(jié)果表明：不考慮共因失效因素的影響會(huì)對(duì)可靠性分析結(jié)果帶來較大的誤差，說明了共因失效對(duì)于交通工具這種重要設(shè)施的安全性影響非常重大，同時(shí)也表明了考慮共因失效的動(dòng)態(tài)故障樹分析方法可為列車安全性及可靠性評(píng)估提供基礎(chǔ)。同時(shí)，本文還提出了各種備份條件下考慮共因失效的動(dòng)態(tài)故障樹及貝葉斯網(wǎng)絡(luò)可靠性建模及評(píng)估方法。建立了考慮共因失效條件下，確定貝葉斯網(wǎng)絡(luò)中各種備件門輸出事件對(duì)應(yīng)節(jié)點(diǎn)的條件概率分布表的方法。通過算例驗(yàn)證了該方法的有效性，并通過與蒙特卡洛數(shù)值仿真方法對(duì)比，驗(yàn)證表明該方法的計(jì)算精度能夠滿足實(shí)際要求。
[Abstract]:With the large-scale, complex and high technology of modern engineering system, system reliability has become the key to restricting the development of complex systems. As the key basic technology for implementing system reliability engineering, the reliability analysis technology is facing some technical difficulties and application challenges brought by the complex system. The reliability analysis technology of complex systems has become one of the hot and difficult problems in the field of reliability engineering. The conventional methods of system reliability analysis mainly include: reliability block diagram, failure mode influence and hazard analysis, fault tree analysis, Petri network method and Monte Carlo numerical simulation method. Generally, the dynamic failure characteristics of the system are not considered, and most of them are based on the independence of the parts and the complete fault data. In the actual complex engineering system, the failure of the parts is usually not independent, and there are many complex correlation and dynamic characteristics, such as the sequence relation of the failure of the components. On the other hand, due to many factors such as cost, time, management and human cause, there are fuzzy uncertainties in the failure data of parts. At present, some achievements have been achieved in the fault tree analysis considering dynamic failure characteristics. However, the fault tree analysis under the condition of fuzzy uncertainty and dynamic failure characteristics is considered simultaneously. The research work is still very short, so that the results obtained by the conventional method are not quite different from the actual situation. Therefore, it is urgent to carry out the study of the system reliability analysis method considering the dynamic failure characteristics and the fuzzy uncertainty of the parts.
In view of the above problems, the following research work has been carried out in this paper.
(1) the dynamic fault tree analysis method based on the fuzzy Markov model. The Markov model method is a state space analysis method, which can accurately describe the failure and maintenance process of the system whose failure distribution and maintenance distribution are all subject to exponential distribution. Based on the Markov model, this paper considers the zero part. The fuzzy uncertainty of the failure information is studied. The dynamic fault tree analysis method under the fuzzy failure rate is studied. By establishing the dynamic fault tree model of the system and using the triangular fuzzy number to describe the failure rate of the parts and systems, the fuzzy Markov model of the system failure process is established by the dynamic fault tree model obtained. Using the idea of the extended principle in the fuzzy theory and the Laplace-Stieltjes transformation to solve the model, the fuzzy failure probability and the fuzzy reliability curve under given membership are obtained. Finally, the fuzzy Markoff model is applied to the reliability modeling and analysis of the hydraulic system of a CNC machining center. It is shown that this method can effectively model and evaluate the reliability of systems with dynamic failure characteristics and fuzzy uncertainties.
(2) based on the dynamic fault tree reliability evaluation model of discrete time Bias network, the system reliability modeling and evaluation method based on the Bias network and dynamic fault tree is studied. The dynamic fault tree model of the system failure is transformed into the Bias network model, and the topology of Bias network is used to express the system. According to the problem of state explosion in the dynamic fault tree solving method based on Markoff model, the condition independence of the Bayesian network is used to reduce the complexity of the model solution. On this basis, a formula for the conditional probability distribution of various logic gates in the static and dynamic fault tree is established. The system failure process and its dynamic characteristics are modeled and analyzed. A dynamic fault tree model and a corresponding Bayesian network model are established based on the satellite solar wing driving mechanism, and the joint tree reasoning algorithm is used to carry out two-way probabilistic reasoning. The example analysis results show that the method can be effectively solved. Reliability analysis and evaluation of complex systems with dynamic failure characteristics.
(3) the method of dynamic fault tree analysis based on continuous time Bayesian networks under fuzzy data. The method of system reliability modeling and analysis based on continuous time Bayesian network based on fuzzy uncertainty is studied. The method of continuous time Bayesian network model can directly obtain the analysis of reliability and failure probability of the system. In this paper, a triangular fuzzy number is used to describe the failure rate of parts and components, and to construct the fuzzy edge failure density function and fuzzy failure distribution function of parts and components. The conditional probability density function and distribution function of the failure events of the non root nodes in Bayesian networks are constructed by using the unit step function and impulse function. The expression of fuzzy edge failure density function and fuzzy failure distribution function of several typical fault tree logic gate output events under the fuzzy failure rate is derived. Finally, the correctness and effectiveness of the method are verified by an example, and the modeling and classification of the rectifier feedback subsystem for the electrical system of a large mining excavator are established. The application of the method in practical engineering system is expounded.
(4) the dynamic fault tree analysis method of common cause failure is considered. The reliability analysis of the system with common cause failure is carried out by the fault tree analysis method. Some classical models and modeling methods in the current common cause failure study are expounded. The fault tree of the rear end accident of a certain EMU is carried out by using the explicit modeling method and the square root model. The failure probability of the system is calculated in two cases, including the common cause failure and the hypothesis component failure. The results show that the influence of the common cause failure factor will bring the greater error to the reliability analysis results, which shows that the common cause failure has a great influence on the safety of the important facilities such as the vehicle. The dynamic fault tree analysis method considering common cause failure can provide a basis for the evaluation of train safety and reliability. At the same time, the dynamic fault tree and Bayesian network reliability modeling and evaluation method considering common cause failure under various backup conditions are proposed. The Bayesian network is established under the condition of common cause failure. The validity of the method is verified by a numerical example, and the results show that the accuracy of the method can meet the actual requirements by comparing with the Monte Carlo numerical simulation method.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH165.3;TP18

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