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研究生:郭家齊
研究生(外文):Chia-Chi Kuo
論文名稱:整合資料探勘與案例式推論於機台故障診斷維護系統之研究
論文名稱(外文):Machine Faults Diagnosis and Maintenance System Using Data Mining and Case Based Reasoning
指導教授:侯東旭侯東旭引用關係
指導教授(外文):Tung-Hsu Hou
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理研究所碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
畢業學年度:92
語文別:中文
論文頁數:80
中文關鍵詞:資料探勘類神經網路案例式推論自然語言決策樹
外文關鍵詞:Decision TreeData MiningArtificial Neural NetworkCase-Based ReasoningNatural Language
相關次數:
  • 被引用被引用:30
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  • 下載下載:215
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本研究以機台設備供應商的觀點,發展一套智慧型遠距診斷與維護系統,透過網路的平台,強化工具機台的售後與後勤服務,當工具機發生故障時候,可以不限於時間和空間限制,立即查明故障原因,加速維修時間。藉以提升顧客價值,提升廠商的競爭能力。
本研究以案例式推論為核心,結合資料探勘演算法建構機台診斷系統。本系統可分成四大部分:自然語言處理程序、案例式推論、決策樹和倒傳遞類神經網路。案例式推論能將維修經驗不斷累積並加以運用,當有新案例產生,透過案例庫的舊案例,提供一個新案例的解決方案。自然語言處理程序讓使用者可以直接用中文描述方式把案例描述出來,讓案例特徵判斷交給系統運作。決策樹從大量的案例庫中建構案例索引,並且把有相似性的案例做歸納,加速推論和搜尋舊案例的速度。倒傳遞類神經網路計算新案例與舊案例間的相似度,減少人為訂定相關特徵權重。本研究並且透過.NET來建構此診斷維護系統。最後,本系統應用於某一射出成型機之故障診斷與維修作業上,並證實其可行性。
In this research, an intelligent network based remote diagnosis and maintenance system is developed to facilitate the machine fault diagnosis and to improve the competitiveness for the prevision machine manufacturers.

The proposed system is basically implemented by using case based reasoning and data mining techniques. The system consists of four subsystems: natural language processing, case based reasoning, decision tree and back-propagation neural network. The natural language processing system allow the user to input the new case to the case based reasoning system in the natural language way. The case based reasoning system is used to diagnose the machine fact and suggest the maintenance procedure based on a similar case that has been stored in the case library. The decision tree system is applied to create the index of machine faults from the case library of the case based reasoning system. The back-propagation neural network is used to train the cases in the decision tree and used to retrieve the best fit case from to cases in the decision tree,

The proposed system is implemented by the .NET and is applied to an injection molding machine to demonstrate its effectiveness.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
一、緒 論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究限制 3
1.4 論文架構 3
二、文 獻 回 顧 與 探 討 5
2.1 診斷維護系統( Diagnosis and Maintenance System ) 5
2.2 自然語言處理( Natural Language Processing ) 6
2.3 案例式推論( Case-Based Reasoning ) 8
2.3.1案例式推論流程 9
2.3.2 案例式推論相關文獻 10
2.4 資料探勘( Data Mining ) 12
2.4.1 資料倉儲( Data Warehouse ) 13
2.4.2 資料探勘相關應用與文獻 14
2.5 類神經網路( Artificial Neural Network ) 15
2.5.1類神經網路種類 16
2.5.2 類神經網路於案例式推論之相關應用與文獻 17
三、研 究 方 法 19
3.1 研究架構 19
3.2 系統推論機制 20
3.3 案例倉儲 22
3.4 自然語言處理 24
3.5 案例索引建立 25
3.6 案例分類 28
3.7 倒傳遞神經網路 29
3.7.1倒傳遞神經網路演算過程 30
3.7.2倒傳遞神經網路計算相似案例 33
四、資料探勘為基礎之遠距機台診斷維護系統 35
4.1 診斷維護系統 35
4.2 後端分析系統 37
4.2.1 字庫字典與專業詞庫建立 38
4.2.2 決策樹模型建立 42
4.2.3 類神經模型建立 48
4.2.3 診斷維護系統驗證 51
4.3 前端使用系統 53
4.3.1 案例輸入 54
4.3.2 案例解決方式取得 55
4.3.3 案例評估 56
五、結 果 與 建 議 59
5.1研究結果 59
5.2 研究建議 60
參 考 文 獻 61
附 錄 65
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