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研究生:顏志霖
研究生(外文):Chih-Lin Yen
論文名稱:壓縮機不穩定現象之偵測:結合模型與信號為基礎之技術
論文名稱(外文):Detection of Compressor Instabilities:Combining Model-Based and Signal-Based Techniques
指導教授:梁耀文梁耀文引用關係
指導教授(外文):Yew-Wen Liang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:104
中文關鍵詞:以模型為基礎以訊號為基礎激喘旋轉失速偵測和診斷自適應性模糊邏輯系統
外文關鍵詞:model-basedsignal-basedsurgerotating stalldetection and diagnosisadptive fuzzy logic system
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本篇論文主要是在探討壓縮機系統不穩定現象之偵測與診斷議題,為了提升偵測效率及診斷可靠度,本論文結合了model-based與signal-based偵測與診斷方法之優點。在model-based部分,由於FIDF偵測方法具有快速及抗雜訊之特性,本論文選用FIDF設計法做為model-based的偵測方法,此外,為提升偵測的可靠度並提供壓縮機系統產生不穩定現象的類型,本論文採用FIDF所產生的殘量利用傅立葉轉換及自適應性模糊邏輯系統作為診斷的工具。模擬結果也驗証了所提出的整合方法之有效性。

This thesis studies the detection and diagnosis issues of compressor’s instabilities. In order to promote the detection efficiency and diagnosis reliability, this thesis combines the advantages of model-based and signal-based fault detection and diagnosis techniques. In this study, Fault Identification Filter (FIDF) design method was adopted as the model-based method because the FIDF design has the features of fast response and insensitivity to noises. In addition, the residual signal produced by FIDF design was employed to improve the detection reliability and to diagnose the type of instabilities by an adaptive fuzzy logic system. Simulation results demonstrate the effectiveness of the proposed scheme.

目 錄
頁次
中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 XI
Chapter 1、緒論….……………………………………………………1
1.1 研究背景…..……………………………………………....1
1.2研究動機……………………………………………………...3
1.3論文架構……………………………………………………...4
Chapter 2、偵測方法簡介………………..…………………….……6
2.1 Model-based方法….………………………………….……6
2.2 Signal-based方法……………………………………….…8
2.2.1 短時距傅立葉轉換(STFT)…………………………………8
2.2.2 維格納分佈(WVD)…………….….………………………12
2.2.3 M波段濾波器組(M-channel filter bank)………………13
2.3 自適應性模糊邏輯系統…………………………………….17
2.3.1 模糊邏輯系統………………………….……………………18
2.3.2 表格學習演算法(table-lookup algorithm)……………22
Chapter 3、壓縮機系統模型與信號法則在不穩定現象偵測之應用…27
3.1 壓縮機系統的簡介…………………………………………..27
3.1.1 簡介……………………………………….……………………27
3.1.2 旋轉失速和激喘..……………………….……………………28
3.2 Moore和Greitzer’s 壓縮機模型…………..…..………29
3.2 過去之相關結果……………………………..…..………32
3.3.1 Model-based的成果….………….…………………32
3.3.2 Signal-based的成果….……………………………37
3.3.2 討論…………………….……………………………43
Chapter 4、模糊邏輯法則在不穩定現象偵測之應用…………….44
4.1 自適應性模糊邏輯系統..………………………..……44
4.1.1 麥克-格拉斯渾沌系統時間序列驗証.…….….………...45
4.1.2 參數的改變增進自適性邏輯系統的性能…………………..56
4.2 雜訊對壓縮機不同偵測法的影響…………………..……62
4.3 結合Model-based與Signal-based方法之偵測與診斷…….…76
4.3.1 FIDF與自適應性模糊邏輯系統的整合偵測..…….…..76
4.3.2 不穩定現象surge或stall的診斷..……………………..84
Chapter 5、結論和未來研究方向…..….…………………………….89
5.1 結論………………………………………………..……….…..89
5.2未來研究方向…………………………..…………………..90
參考文獻………………………………………………………………….92

參考文獻
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