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研究生:辜志偉
研究生(外文):Jhih-Wuei Gwu
論文名稱:類神經網路應用於旋轉機械故障診斷系統之研究
論文名稱(外文):Study of Rotating Machine Fault Diagnosis System Using Neural Network
指導教授:洪瑞鴻洪瑞鴻引用關係
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:機械與輪機工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:62
中文關鍵詞:類神經網路旋轉機械故障診斷頻譜分析
外文關鍵詞:neural networkrotating machinefault diagnosisspectrum analysis
相關次數:
  • 被引用被引用:4
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由於工廠自動化與人力精簡使得工業界對於機械的故障診斷系統越來越重視,傳統的數學模式故障診斷分析,對於複雜而又非線性的系統顯得相當不容易設計。類神經網路因為具有學習訓練的能力以及對於微小變化的容錯能力,因此特別適合應用於複雜的非線性系統。
本論文以Levenberg-Marquardt方法所得的網路參數,經過倒傳遞演算法則,來判斷旋轉機械之運轉狀態。利用模擬類似人類雙耳聽覺的方式,使用多個麥克風系統抓取旋轉機械所產生之聲音震動信號,透過頻譜分析技術從頻域中抽取信號特徵,並使用類神經網路作出準確的故障診斷。本系統能夠在螢幕上透過圖形化人機介面,即時地顯示各馬達是否在正常運轉狀態。不僅針對單一馬達作故障即時診斷,並且擴展到能同時診斷數個旋轉機械之運轉狀態。
Machine fault diagnosis system was to be respected for industry as a result of producing automatically with less and less operators. It is not easy to design fault diagnosis system for a complex and non-linear system with traditional mathematical module analysis. Neural network has the learning ability from training and tolerance for a slight change, and therefore it is more suitable for the complex and non-linear system especially.
In this thesis, the network parameters trained from Levenberg-Marquardt method are to be used to classify the condition of rotating machine by back propagation algorithm. Simulates bi-directional acoustic emission like human hearing and employs multi-microphones system to acquire vibration sound signal brought on rotating machine. The spectrum analysis technique can extract signal features from frequency domain and these features were to be taken as input of neural network that can diagnose fault accurately. The diagnosis system in this study can detect motor condition on-line whether in normal state or not by using graphical user interface. It can diagnose not only single motor condition on-line, but also extend to several motors conditions at the same time.
致  謝………………………………………………………… i
中文摘要………………………………………………………… ii
英文摘要………………………………………………………… iii
目  錄………………………………………………………… iv
第一章 緒論
1-1 前言 …………………………………………………… 1
1-2 研究動機 ……………………………………………… 2
1-3 文獻回顧 ……………………………………………… 3
1-4 內容架構 ……………………………………………… 4
第二章 數位信號處理
2-1 信號分類 ……………………………………………… 5
2-2 頻譜分析 ……………………………………………… 7
2-3 信號取樣 ……………………………………………… 8
2-4 A/D轉換器規格………………………………………… 9
2-5 窗口法 ………………………………………………… 11
2-6 濾波器 ………………………………………………… 11

第三章 類神經網路
3-1 類神經網路概述 ……………………………………… 13
3-2 倒傳遞演算法 ………………………………………… 14
3-3 Levenberg-Marquardt方法…………………………… 16
第四章 LabVIEW與MATLAB軟體介紹
4-1 LabVIEW程式語言……………………………………… 20
4-2 MATLAB類神經網路工具箱 …………………………… 22
第五章 實驗設備與故障診斷系統設計
5-1 實驗設備 ……………………………………………… 24
5-2 故障診斷系統設計 …………………………………… 28
5-3 數位信號分析 ………………………………………… 29
第六章 程式架構與流程
6-1 信號特徵抽取程式 …………………………………… 30
6-2 類神經網路訓練程式 ……………………………… 30
6-3 即時線上故障診斷程式 ……………………………… 31
第七章 測試與討論
7-1 測試結果 …………………………………………… 33
7-2 討論分析 …………………………………………… 34
第八章 結論
8-1 研究成果 …………………………………………… 38
8-2 未來展望 …………………………………………… 38
參考文獻 ………………………………………………………… 40
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