本文選題：失效模式 + 影響及危害性分析�。� 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：潛在失效模式、影響及危害性分析(Failure Modes,Effects and Criticality Analysis,FMECA)方法是一項(xiàng)在出售產(chǎn)品給客戶之前,識(shí)別、確認(rèn)和消除在系統(tǒng)、設(shè)計(jì)、過程和服務(wù)中存在的已知的和潛在的失效、故障、錯(cuò)誤、問題等的工程技術(shù)。該技術(shù)應(yīng)用于系統(tǒng)、設(shè)計(jì)、過程和服務(wù)的初期,利用現(xiàn)有的部分有效數(shù)據(jù)、歷史相關(guān)文件信息以及來自各相關(guān)領(lǐng)域的專家所組成的FMECA小組成員的專業(yè)知識(shí)與經(jīng)驗(yàn),在風(fēng)險(xiǎn)發(fā)生前進(jìn)行失效模式的查找與分析,進(jìn)而造成了 FMECA方法特殊的應(yīng)用環(huán)境——高度不確定的分析環(huán)境,其中包括:無法獲得全部有效數(shù)據(jù)所造成的模糊不確定性;FMECA小組成員復(fù)雜多樣的專業(yè)背景造成的專家成員之間的模糊不確定性;FMECA小組成員自身對(duì)失效模式判斷的模糊不確定性。這種復(fù)雜的不確定性不可避免的造成了 FMECA方法量化分析部分的失真、失準(zhǔn)問題。在此應(yīng)用背景下,本文提出能夠量化模糊不確定性的梯形區(qū)間二型模糊FMECA方法,提高了 FMECA方法量化分析的準(zhǔn)確性和有效性。具體研究方法及主要研究成果有:(1)構(gòu)建了信息的量化模型。首先,采取符合人類思考及判斷能力的區(qū)間數(shù)作為初始個(gè)體判斷信息的模糊不確定性量化數(shù)據(jù);然后,通過對(duì)群體給出的這組區(qū)間數(shù)進(jìn)行累積和堆疊,找到群體判斷中隸屬程度最高的判斷區(qū)間作為群體判斷在真值;同時(shí)兼顧除此之外的差異性數(shù)據(jù),這部分?jǐn)?shù)據(jù)信息記錄了個(gè)體的不確定性以及個(gè)體之間的不確定性差異;最終構(gòu)建出記錄了群體判斷共識(shí)與差異的梯形區(qū)間二型模糊群體決策值,并以該值作為進(jìn)一步FMECA分析的風(fēng)險(xiǎn)因素評(píng)價(jià)數(shù)據(jù)。(2)提出了梯形區(qū)間二型模糊綜合排序法。通過拆解梯形區(qū)間二型模糊的隸屬函數(shù)從兩部分分別進(jìn)行比較:一部分是該模糊數(shù)所代表的真值部分,也就是模糊數(shù)隸屬程度最高的部分,采用重心比較法進(jìn)行比較;另一部分是該模糊數(shù)所記錄的模糊不確定性部分,也就是模糊數(shù)隸屬程度較低且較為分散的部分,采用基于相對(duì)幾何空間大小及位置構(gòu)建的針對(duì)隸屬函數(shù)離散部分的比較法進(jìn)行比較。綜合兩部分比較結(jié)果,并記錄在不同數(shù)量級(jí)上,使得比較結(jié)果既保護(hù)了模糊數(shù)真值的有效性又充分考慮了模糊不確定性程度對(duì)該模糊數(shù)的影響。(3)提出基于梯形區(qū)間二型模糊的FMECA方法。通過構(gòu)建的模糊信息量化模型,處理FMECA方法定性分析中的模糊不確定性并聚合成群體判斷評(píng)價(jià)數(shù)據(jù)。再通過梯形區(qū)間二型模糊綜合比較法,從兩個(gè)部分對(duì)風(fēng)險(xiǎn)因素的群體評(píng)價(jià)值進(jìn)行比較。最后,根據(jù)梯形區(qū)間二型模糊FMECA方法的風(fēng)險(xiǎn)優(yōu)先系數(shù)(Risk Priority Number,RPN)計(jì)算值找到高風(fēng)險(xiǎn)、需優(yōu)先解決的失效模式,并完成完整的梯形區(qū)間二型模糊FMECA方法。
[Abstract]:Potential failure mode, impact and hazard analysis FMECAA method is a method of identifying, confirming and eliminating known and potential failures, errors in systems, designs, processes and services before selling products to customers. Engineering techniques for problems, etc. The technology is applied in the early stages of systems, designs, processes and services, using existing partially valid data, historical documentation information, and the expertise and experience of FMECA team members from various related fields. The failure mode is searched and analyzed before the risk occurs, which leads to the special application environment of FMECA method, which is highly uncertain analysis environment. It includes: the fuzzy uncertainty caused by the failure to obtain all valid data and the fuzzy uncertainty among the expert members caused by the complicated and diverse professional background of FMECA members. This complex uncertainty inevitably leads to distortion and misalignment in the quantitative analysis of the FMECA method. In this context, a trapezoidal interval type fuzzy FMECA method, which can quantify fuzzy uncertainty, is proposed in this paper, which improves the accuracy and effectiveness of the quantitative analysis of the FMECA method. The specific research methods and main research results are: 1) the quantitative model of information is constructed. First, the fuzzy uncertain quantitative data of the initial individual judgment information is taken according to the interval number of human thinking and judgment ability, and then the group interval number is accumulated and stacked by the group. Find the highest degree of membership in group judgment as the true value of group judgment, and take into account the difference of the other data, this part of the data information records the uncertainty of individuals and the uncertainty difference between individuals. Finally, a trapezoidal interval type 2 fuzzy group decision value, which records the consensus and difference of the group judgment, is constructed and used as the risk factor evaluation data for further FMECA analysis. (2) the trapezoidal interval 2 fuzzy comprehensive ranking method is proposed. The membership function of trapezoidal interval type 2 fuzzy membership is compared from two parts: one part is the true value part represented by the fuzzy number, that is, the part with the highest membership degree of fuzzy number, and the center of gravity comparison method is used to compare; The other part is the fuzzy uncertainty part recorded by the fuzzy number, that is, the part with lower membership degree and more dispersion of the fuzzy number. A comparison method for discrete parts of membership function is proposed based on relative geometric space and position. Synthesizing the results of the two parts, and recording them on different orders of magnitude, The comparison results not only protect the validity of the true value of fuzzy number but also fully consider the influence of the degree of fuzzy uncertainty on the fuzzy number. (3) A FMECA method based on trapezoidal interval type 2 fuzzy method is proposed. The fuzzy information quantization model is constructed to deal with the fuzzy uncertainty in the qualitative analysis of FMECA method and to aggregate the evaluation data of group judgment. Then through trapezoidal interval type 2 fuzzy comprehensive comparison method, the group evaluation values of risk factors are compared from two parts. Finally, according to the risk Priority number FMECA calculated value of trapezoidal interval type 2 fuzzy FMECA method, the high risk and priority failure mode is found, and the complete trapezoidal interval type 2 fuzzy FMECA method is completed.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F273.2

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本文編號(hào)：1855399