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研究生:張育維
研究生(外文):Yuh-Wei Jang
論文名稱:範例庫推論技術之改良及其在失效模式與效應分析上之應用
論文名稱(外文):An improved inference technique for case-based reasoning and its application to failure modes and effects analysis
指導教授:鄭啟斌鄭啟斌引用關係
指導教授(外文):Chi-Bin Cheng
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:88
中文關鍵詞:失效模式與效應分析範例庫推論技術特徵屬性相似度衡量法模糊相似度
外文關鍵詞:FMEACBRcharacteristic attributesadjustment techniquesimilarity measurements
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摘要
失效模式與效應分析(Failure modes and effects analysis , FMEA)是一種品質改善的預防性技術。由於QS9000產品系統之風行,對於實施FMEA之需求亦日漸增加。FMEA的實施是一件勞力密集且十分耗時的工作,而且在花費這麼多努力的情況下,通常仍然無法獲得很好的效果。其原因在於一個完善的FMEA需要許多各部門專家的長期投入,而且現代產品的複雜性使得分析工作更難進行。本研究之目的即在於建立一個輔助FMEA實行的系統,以降低實施者之負擔。每一個公司雖然產品眾多,但是在產品間通常有某種程度的相似性。因此,本研究選用範例庫推論技術(Case based reasoning , CBR)來建立FMEA之輔助系統。藉由對所有產品粹取其特徵屬性來建立範例庫,然後再以推論方法由範例庫中搜尋與新產品最相似之舊產品,以作為新產品FMEA之參考。
範例庫之推論乃是以相似度衡量法的方式進行。針對以往相似度衡量法之優缺點,吾人提出一個改良的衡量方法。在新的衡量法中,吾人不但引進模糊相似度的概念,並檢討以往衡量方法的傾向,對於傾向高估或低估的方法調整其推論結果。此外吾人並對範例庫中各特徵屬性賦予不同的重要度,並模擬人類思維模式予以加權轉換。
為了驗證本研究方法之效果,吾人以國內某釘槍工司之產品作為範例庫,並製作使用者介面。雖然由本研究方法推論與人工選取的結果一致,但是當舊產品眾多時,本方法勢必可節省大量時間。
關鍵名稱:,範例庫推論技術,特徵屬性,
相似度衡量法,模糊相似度。
Abstract
Failure modes and effects analysis (FMEA) is a preventive method for quality improvement. Due to the prevalence of QS9000 system, the needs of implementing FMEA is growing. Implementation of FMEA is labor-intensive and time-consuming, and it requires domain knowledge of experts from many disciplines. The high complexity of modern products make the implementation even more difficult. The purpose of this study is to build an assistant system for the implementation of FMEA so as to alleviate such difficulties. Considering the similarity existing among products of a certain company, case based reasoning (CBR) is chosen to build this assistant system. The steps are first constructing a case base by capturing characteristic attributes of products, and then infer the most similar old product with the new product from the case base.
The inference process of CBR is carried out by a similarity measurement. In this study, we propose a new and improved approach from the old similarity measure methods. In this new approach, we correct the over-estimating or under-estimating tendency of some old similarity measure methods with an adjustment technique. Moreover, the importance of different characteristic attributes in the case base is emphasized, and a weighted technique, which mimics human thinking, conducted on characteristic attributes is suggested.
To justify the performance of our approach, an example of air nailers is taken from a company in Taiwan. A case base is constructed upon many old products of this company, and a user interface written in Visual Basic is also provided. Comparing with the new product, a most similar old product is picked from the case base by our inference approach. Meanwhile, an expert is also asked to pick out the most similar one among the old products based on his own experience. The result by our approach and by the expert is consistent. However, when the amount of old products is huge, our approach has the advantage of saving time significantly.
目 錄
中文摘要……………………………………………………………………..I
英文摘要…………….…………………………………………………..III
誌謝………………………………………………………………………….IV
目錄…………………………………………………………….…………..V
圖目錄………………..…………………………………..………..VII
表目錄……………………………………………………………………VIII
第一章序論………….………………………………………….….….1
第一節研究背景..….……………………………….…….….....…1
第二節研究動機…..………………………………..……...…..….7
第三節研究目的與方法…………………………………….....…...8
第二章專家系統在FMEA之應用……..……………………....……..11
第一節FMEA之實施困難及解決辦法……………………........…..11
第二節專家系統之推論技術……………………………......……..14
第三節範例庫推論系統………………………………………........16
第四節CBR之推論技術……………………………………….........19
第五節模糊集合之相似度衡量………………………………........24
.
第三章範例庫之推論-相似度衡量方法………………………….…..27
第一節範例庫之建立………………………………..………........28
第二節相似度之衡量……………………………………….......….32
第三節整體相似度之推論………………………………….….......43
第四章 實例驗證…………………..….……………………………….54
第一節 範例庫之建立…………………………………………...…54
第二節 範例庫搜尋……………….………...………………..….63
第三節 推論效率比較……………….…………..……..………..69
第五章 結論與建議……………………………………...…………..71
第一節結論…………………………………………………………...71
第二節 未來研究方向與建議…………………………………………74
參考文獻…………………………………………………………....…..76
附錄1……………………………………………………………….......79
附錄2……………………………………………………………….....…84
圖目錄
圖2-1 CBR實行之流程圖……………………………………………18
圖3-1子相似度值之加權轉換函數圖(1)………………………..45
圖3-2子相似度值之加權轉換函數圖(2)………………………..47
圖3-3子相似度值之加權轉換函數圖(3)………………………..49
圖4-1歸屬函數圖形(釘槍價格)………………………………....58
圖4-2歸屬函數圖形(釘槍力道)………………………………....59
圖4-3 歸屬函數圖形(釘槍重量)……………………………....……60
圖4-4 軟體原始介面…………………………………………..…….65
圖4-5 各屬性值與重要度之輸入…………………...………………65
圖4-6 軟體推論結果………………………………………...…..…..66
表目錄
表3-1 範例之特徵屬性與屬性值………………………………….29
表3-2 模糊化後之範例庫………………………………………….33
表3-3 各衡量法之相似度推論結果………………….……………34
表3-4 各法對於各定量屬性相似度衡量上之誤差值…………….36
表3-5 範例庫於定量屬性之相關模糊集合與歸屬程度………….39
表3-6 目前問題於各定量屬性之相關模糊集合與其歸屬程度….39
表3-7 各法之推論結果(定量屬性 )…….………………….....40
表3-8 各法之推論結果(定量屬性 )……...…………………..40
表3-9 各法之推論結果(定量屬性 )………...………………..41
表3-10 各衡量法之誤差值……………………….………………..41
表3-11 目前問題與各範例之子相似度值……………………...…51
表3-12 各特徵屬性之重要度表………………………………...…52
表3-13 子相似度加權值與整體相似度指標…………………...…53
表4-1 釘槍定性屬性值代碼表……………………..………...……55
表4-2 釘槍範例庫(定性屬性部分)……………..……..……….56
表4-3 釘槍範例庫(定量屬性部分)………………….…………61
表4-4 釘槍範例庫(定量屬性之模糊子集合與歸屬程度)…….62
表4-5 目前問題之定性屬性表………………………………….…64
表4-6 目前問題之定量特徵屬性表……………..…………………...64
表4-7 目前問題於各定量特徵屬性之模糊子集合與歸屬程度表.…64
表4-8 各特徵屬性之重要度表………….……………………………65
表4-9 步驟一之結果……………………………………….………....67
表4-10 步驟二與三後之結果………………………..…………….…68
中文部分
[1]王宗華,『可靠度工程技術手冊』,中華民國品質協會,民國87年。
[2]陳紹興,『失效模式效應分析作業』,經濟部工業局中心衛星工廠制度推動小組,民國77年。
[3]陳紹興,鄭燕琴,『品質保證』,中華民國品質管制學會,民國72年。
[4]陳增儒,『模糊關連記憶神經網路應用於半導體晶圓之FMEA決策
支援系統設計』,元智大學工業工程與管理所碩士論文,民國88年。
[5]鄭春生,『品質管理』,育友圖書有限公司,民國86年。
[6]鄭福,『專家系統概論』, 第三波,民國76年。
英文部分
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[10] Gerstenkorn, T. and Man''ko, J., ”Correlation of intuitionistic fuzzy sets,” Fuzzy Sets and Systems, Vol. 44, pp. 39-43(1991).
[11] Hunt, J. E., Price, C. J. and Lee, M. H., “Automating the FMEA process,” Intelligent Systems Engineering Journal, Vol. 2, No. 2, pp. 119-132(1993).
[12] Hyung, L.K., Song, Y. S. and Lee, K. M., ”Similarity measure between fuzzy sets and between elements,” Fuzzy Sets and Systems, Vol. 62, pp. 291-293(1994).
[13] Jeng, B. C. and Liang, T. P., ”Fuzzy indexing and retrieval in case-based system, ” Expert Systems with Applications, Vol. 8, No. 1, pp.135-142(1995).
[14] Kosko, B., “fuzzy cognitive maps,” International Journal man-machine studies, Vol. 24, pp. 65-75(1986).
[15] Lee, D. and Lee, K. H., ”An approach to case-based system for conceptual ship design assistant,” Expert Systems with Applications, Vol. 16, pp. 97-104 (1999).
[16] Pappis, C. P. and Karacapilidis, N. I., ”A comparative assessment of measure of similarity of fuzzy value,” Fuzzy Sets and Systems, Vol. 56, pp. 171-174 (1993).
[17] Pelaez, C. E. and Bowles, J. B., “Using fuzzy cognitive maps as a system model for failure modes and effects analysis,” Information science, Vol. 88, pp. 177-199(1996).
[18] Sun, S. H., and Chen, J. L., “A fixture design system using case-based reasoning, ” Engineering Applications of Artificial intelligence , Vol. 9, No. 5, pp. 533-504(1996).
[19] Wang, W. J., “New similarity measures on fuzzy sets and on elements,” Fuzzy Sets and Systems, Vol. 85, pp. 305-309(1997).
[20] Watson, I., “Case-based reasoning is a methodology not a technology,” Knowledge – based systems, Vol. 12, pp. 303-308(1999).
[21] Wirth, R., Berthold, B., Kramer, A. and Peter, G., “knowledge-based support of system analysis for the analysis of failure modes and effects,” Engineering Applications of Artificial Intelligence, Vol. 9, No. 3, pp. 219-229(1996).
[22] Yau, N. J. and Yang, J. B., ”Applying case-based reasoning technique to retaining wall selection,” Automation in construction, Vol. 7, pp. 271-283 (1998).
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