【正文】 lseu5=0。enduu3=[u1 u2 u3 u4 u5] %定義評(píng)價(jià)指標(biāo)可檢測(cè)度(D)的評(píng)價(jià)向量uu=[uu1。uu2。uu3] %定義模糊關(guān)系矩陣A=[ ]。%確定三個(gè)評(píng)價(jià)指標(biāo)的權(quán)重系數(shù)B=A*uu %確定模糊綜合評(píng)價(jià)向量 b=max(B) %確定風(fēng)險(xiǎn)等級(jí) 本章小結(jié)本章介紹了MATLAB軟件及其語(yǔ)言，并且對(duì)上文提出的引入權(quán)重系數(shù)和模糊理論的風(fēng)險(xiǎn)評(píng)估流程進(jìn)行了MATLAB語(yǔ)言的編程。MATLAB使本文涉及到矩陣計(jì)算的三個(gè)評(píng)價(jià)指標(biāo)權(quán)重系數(shù)的確定以及模糊關(guān)系矩陣等的計(jì)算快速簡(jiǎn)單。第5章 結(jié)論與展望為了提高變壓器安全，經(jīng)濟(jì)運(yùn)行水平，合理的對(duì)變壓器進(jìn)行風(fēng)險(xiǎn)評(píng)估并據(jù)此制定維修策略，本文查閱了大量中外文相關(guān)文獻(xiàn)，提出了基于FMEA的變壓器風(fēng)險(xiǎn)評(píng)估。研究的主要結(jié)論如下：(1)在對(duì)變壓器的結(jié)構(gòu)和功能分析的基礎(chǔ)上，完成了變壓器套管的故障模式與效應(yīng)分析表。為下文變壓器的風(fēng)險(xiǎn)評(píng)估奠定了基礎(chǔ)。(2)在變壓器故障模式與影響分析的基礎(chǔ)上，提出了變壓器的風(fēng)險(xiǎn)評(píng)估體系，本文采用三個(gè)評(píng)價(jià)指標(biāo)故障嚴(yán)重度(S)、發(fā)生概率(O)、和可檢測(cè)程度(D)來(lái)對(duì)變壓器的風(fēng)險(xiǎn)進(jìn)行評(píng)估，傳統(tǒng)FMEA通過(guò)風(fēng)險(xiǎn)優(yōu)先數(shù)RPN來(lái)確定故障的風(fēng)險(xiǎn)等級(jí)，但是此種方法有一定的缺陷，傳統(tǒng)評(píng)估方法存在人為因素影響、不區(qū)分各指標(biāo)相對(duì)重要度以及因風(fēng)險(xiǎn)優(yōu)先級(jí)指數(shù)相同而無(wú)法評(píng)價(jià)等缺點(diǎn)，為了解決這些缺陷使變壓器的風(fēng)險(xiǎn)評(píng)估更為科學(xué)客觀，本文提出對(duì)三個(gè)評(píng)價(jià)指標(biāo)進(jìn)行模糊化處理并確定三個(gè)評(píng)價(jià)指標(biāo)權(quán)重系數(shù)。最后得到改進(jìn)的變壓器風(fēng)險(xiǎn)評(píng)估方法的一般步驟。并用實(shí)例說(shuō)明風(fēng)險(xiǎn)評(píng)估過(guò)程。(3)有關(guān)評(píng)價(jià)指標(biāo)的模糊化以及權(quán)重系數(shù)的確定涉及到矩陣的運(yùn)算，運(yùn)用MATLAB可以方便的解決矩陣計(jì)算。目前，有關(guān)電力變壓器風(fēng)險(xiǎn)評(píng)估方面的研究還處于起步階段。本文基于FMEA對(duì)引入模糊理論和權(quán)重系數(shù)的電力變壓器風(fēng)險(xiǎn)評(píng)估方法和模型進(jìn)行了研究并取得了一定的研究成果，但是在有效反映電力變壓器風(fēng)險(xiǎn)的特征參量的篩選和提取、更為有效的評(píng)估方法和評(píng)估模型的應(yīng)用等方面所涉及的諸多相關(guān)問(wèn)題都有待更進(jìn)一步研究：(1)通過(guò)對(duì)變壓器結(jié)構(gòu)和系統(tǒng)結(jié)構(gòu)的研究，進(jìn)一步細(xì)分故障模式與影響分析。(2)將不同的評(píng)估體系和評(píng)估模型應(yīng)用于電力變壓器的風(fēng)險(xiǎn)評(píng)估。對(duì)這些模型進(jìn)行細(xì)致的分析和比較，尋找最有效的評(píng)估變壓器風(fēng)險(xiǎn)評(píng)估的模型和方法。(3)對(duì)變壓器評(píng)價(jià)指標(biāo)、指標(biāo)的權(quán)重確定方法進(jìn)行深入研究，結(jié)合現(xiàn)場(chǎng)專(zhuān)家的經(jīng)驗(yàn)和數(shù)學(xué)知識(shí)，探索指標(biāo)權(quán)重科學(xué)有效的確定方法，對(duì)各評(píng)價(jià)指標(biāo)在總體評(píng)估中的作用進(jìn)行動(dòng)態(tài)權(quán)衡，使風(fēng)險(xiǎn)評(píng)估結(jié)果更加客觀、可靠。參考文獻(xiàn)[1] 王卓甫. 工程項(xiàng)目風(fēng)險(xiǎn)管理一理論方法與應(yīng)用[M]. 北京: 中國(guó)水利水電出版, 2002.[2] 戴樹(shù)和. 工程風(fēng)險(xiǎn)分析技術(shù)[M].北京: 化學(xué)工業(yè)出版社, 2007.[3] 魏新利, 李惠萍, 王自健. 工業(yè)生產(chǎn)過(guò)程安全評(píng)價(jià)[M]. 北京: 化學(xué)工業(yè)出版社, 2005.[4] 祝效華, 童華, 劉清友等. 基于故障樹(shù)的套管失效模糊綜合評(píng)判分析模型[J]. 石油機(jī) 械, 2004, 32(2): 17~19.[5] J. 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Lee. Failure mode and effects analysis using fuzzy method and grey method [J]. Kybernetes, 1999, 28(9): 1072~1080. 附 錄附錄A 外文翻譯原文On Fuzzy inference system basedFailure Mode and Effect Analysis (FMEA) methodologyKai Meng TayElectronic Engineering Department, Faculty of Engineering,University Malaysia SarawakSarawak, Malaysiakmtay@AbstractFilure Mode and Effect Analysis (FMEA) is a popular problem prevention methodology. It utilizes a Risk Priority Number (RPN) model to evaluate the risk associated to each failure mode. The conventional RPN model is simple, but, its accuracy is argued. A fuzzy RPN model is proposed as an alternative to the conventional RPN. The fuzzy RPN model allows the relation between the RPN score and Severity, Occurrence and Detect ratings to be of nonlinear relationship, and it maybe a more realistic representation. In this paper, the efficiency of the fuzzy RPN model in order to allow valid and meaningful parisons among different failure modes in FMEA to be made is investigated. It is suggested that the fuzzy RPN should be subjected to certain theoretical properties of a length function . monotonicity, subadditivity and etc. In this paper, focus is on the monotonicity property. The monotonicity property for the fuzzy RPN is firstly defined, and a sufficient condition for a FIS to be monotone is applied to the fuzzy RPN model. This is an easy and reliable guideline to construct the fuzzy RPN in practice. Case studies relating to semiconductor industry are then presented. Keywords: Fuzzy inference system, monotonicity property, sufficient conditions, FMEA, manufacturingⅠ NTRODUCTIONFailure Mode and Effect Analysis (FMEA) is an effective problem prevention methodology that can easily interface with many engineering and reliability methods [1]. It can be described as a systemized group of activities intended to recognize and to evaluate the potential failures of a product/process and its effects [2]. Besides, FMEA identifes actions which can eliminate or reduce the chances of potential failures from recurring. It also helps users to identify the key design or process characteristics that require special controls for manufacturing, and to highlight areas for improvement in characteristic control or performance [1].Conventional FMEA use a Risk Priority Number (RPN) to evaluate the risk associated to each failure mode. A RPN is a product of the risk factors, ., Severity (S), Occurrence (O) and Detect (D). FMEA assumes that multiple failure modes exist, and each failure mode has a different risk level that have to be evaluated, and ranked. In general, S, O and D are of integer 1 to 10, usually defined in scale tables. From literature, the use of Fuzzy Inference System (FIS) in FMEA is not new. Bowles and Pelez suggest to replace the conventional RPN model with a FIS (fuzzy RPN model) [3]. The fuzzy RPN mode