【正文】 2434滲漏油高5725導桿分流低166載流部分接觸不良中2107均球壓懸浮放電高572由上表可以得到基于傳統(tǒng)FMEA方法和引入模糊理論和權(quán)重系數(shù)方法下的變壓器套管的幾種故障模式的風險等級，并對兩種方法下的風險等級進行比較。還引入模糊理論對各評價指標模糊化處理，避免了評價過程中指標的主觀性和隨意性。它在數(shù)學科技應(yīng)用軟件中在數(shù)值計算方面首屈一指。1984年由Little、Moler、Steve Bangert合作成立了的MathWorks公司正式把MATLAB推向市場。 MATLAB系統(tǒng)結(jié)構(gòu)MATLAB系統(tǒng)由MATLAB開發(fā)環(huán)境、MATLAB數(shù)學函數(shù)庫、MATLAB語言、MATLAB圖形處理系統(tǒng)和MATLAB應(yīng)用程序接口(API)五大部分構(gòu)成。新版本的MATLAB語言是基于最為流行的C++語言基礎(chǔ)上的，因此語法特征與C++語言極為相似，而且更加簡單，更加符合科技人員對數(shù)學表達式的書寫格式。②用戶使用方便：MATLAB語言是一種解釋執(zhí)執(zhí)行的語言(在沒被專門的工具編譯之前)，它靈活、方便，其調(diào)試程序的手段豐富。又如，將MATLAB源程序編輯為M交件，由于MATLAB磁盤文件也是M文件，所以編輯后的源文件就可直接運行，而不需進行編譯和連接。于是MATLAB可以很方便地移植到能運行C語言的操作平臺土。因此，不久的將來，它一定能名副其實地成為“萬能演算紙式的”科學算法語言。同時對一些特殊的可視化要求，例如圖形對話等，MATLAB也有相應(yīng)的功能函數(shù)，保證了用戶不同層次的要求。MATLAB的一個重要特色就是具有一套程序擴展系統(tǒng)和一組稱之為工具箱的特殊應(yīng)用子程序。在MATLAB中，選擇結(jié)構(gòu)可由以下語句來實現(xiàn)：if語句if語句的最簡單用法為：　if’) elseif MATLAB在本文的應(yīng)用本文研究了基于FMEA的電力變壓器風險評估，在評價指標模糊化和權(quán)重系數(shù)確定時涉及到矩陣的計算，為了節(jié)省計算時間，用MATLAB語言對風險評估的過程進行了編程。endif x2amp。x=8u3=*x。x=10 %定義模糊隸屬函數(shù)“很高”u5=*x4。elseif x4amp。endif x6amp。elseu5=0。else u2=0。elseif x8amp。uu2。最后得到改進的變壓器風險評估方法的一般步驟。參考文獻[1] 王卓甫. 工程項目風險管理一理論方法與應(yīng)用[M]. 北京: 中國水利水電出版, 2002.[2] 戴樹和. 工程風險分析技術(shù)[M].北京: 化學工業(yè)出版社, 2007.[3] 魏新利, 李惠萍, 王自健. 工業(yè)生產(chǎn)過程安全評價[M]. 北京: 化學工業(yè)出版社, 2005.[4] 祝效華, 童華, 劉清友等. 基于故障樹的套管失效模糊綜合評判分析模型[J]. 石油機 械, 2004, 32(2): 17~19.[5] J. 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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, MalaysiakmtayAbstractFilure 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 model