(3.236.228.250) 您好!臺灣時間:2021/04/22 03:56
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:羅永傑
研究生(外文):Yung-Chieh Lo
論文名稱:應用振動訊號之主成份分析與K均值分群法於感應電動機主動式運轉狀態監測
論文名稱(外文):Vibration Signal-Based Proactive Operation Condition Monitoring of Induction Motors Using Principal Component Analysis and K-means Clustering
指導教授:張宏展
指導教授(外文):Hong-Chan Chang
口試委員:張宏展吳瑞南郭政謙陳鴻誠
口試委員(外文):Hong-Chan ChangRuay-Nan WuCheng-Chien KuoHong-Chen Chen
口試日期:2019-07-22
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
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:2019
畢業學年度:107
語文別:中文
論文頁數:85
中文關鍵詞:感應電動機運轉狀態監測主成份分析K均值分群法振動主動式
外文關鍵詞:Induction MotorCondition MonitoringPCAK-meansVibraitonProactive
相關次數:
  • 被引用被引用:1
  • 點閱點閱:72
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
感應電動機具有價格低廉、堅固耐用的特性,一直以來在工業發展上扮演著很重要的角色,在任何工廠都是不可或缺的動力來源。以往在運維策略上,大部分的工廠都是採取定期性維護保養或是狀態性維護保養。近年來,受益於大數據分析,可利用機器學習之技術發現潛期的故障及早進行維修,將保養方式提升為主動性維護保養。
本論文以人為加工之方式製造四種實驗模型,量測實驗模型之三軸加速度,可從中取得運轉狀態為「正常」和「警戒」的資料,但運轉狀態為「危險」的資料無法藉由實際實驗模型取得,因此本研究選擇將量測到的數據線性放大固定倍數,使其超過振動規範VDI-2056之門檻,來模擬運轉狀態為「危險」的資料。將量測到的三軸加速度經過訊號處理和計算後得到振動參數,通過將這些參數以主成份分析進行篩選,再將篩選過後的參數進行K均值分群法,評估其分群效果。最後,本研究以四種案例進行分析,共有16種分群結果,根據分群結果找出適合用於主動式運轉狀態監測之參數共5個,以振動規範來進行驗證,發現分類的準確率皆為100%。
Inductive motors are inexpensive, rugged and have always played an important role in industrial development. They are indispensable power sources in the factory. In the past, factories often adopt O&M (Operation and maintenance) strategies based on “Time-Based Maintenance” or “Condition-Based Maintenance”. In recent years, benefiting from big data analysis, we can identify the potential faults and fix it early by machine learning. This is the so-called proactive maintenance.
In this thesis, we first manufacture four customer-made experimental models and measure the triaxial signals from these models. We get the data of opearation condition of “Normal” and “Warning” from the afore-mentioned experimental models. In real opereation, we cannot get the data of opearation condition of “Danger”. Alternatively, we linearly amplify the measurement data to simulate the danger opearating condition data, according to the VDI-2056 standrad. We obtain vibration parameters from triaxial signals by signal processing and caculation, and extract feature parameters by principal component analysis (PCA). Then, according to the extracted parameters, classify the operating status by the K-means clustering. Finally, 4 case studies conducted and 16 clustering results were obtained. We find out that there are 5 parameters suitable for proactive condition monitoring. Results obtained from simulations show that the classifying accuracy for different operation conditions is 100%.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 X
第1章、 緒論 1
1.1 研究背景與動機 1
1.2 研究方法 4
1.3 文獻回顧 7
1.4 章節簡述 8
第2章、 運轉狀態資料之擷取與模擬 9
2.1 前言 9
2.2 振動相關規範 9
2.2.1 ISO 10816 9
2.2.2 IEC 60034-14 12
2.2.3 VDI 2056 13
2.3 感應電動機實驗模型 14
2.3.1 定子故障之實驗模型 15
2.3.2 轉子故障之實驗模型 16
2.3.3 軸承故障之實驗模型 16
2.3.4 不對心故障之實驗模型 17
2.4 實驗模型之資料量測 18
2.4.1 測量平台架構 18
2.4.2 感測器簡介 19
2.4.3 訊號擷取 19
2.4.4 訊號處理 20
2.5 振動監測參數 23
2.5.1 時域型特徵參數 23
2.5.2 頻域型特徵參數 25
2.6 運轉狀態之劃分 26
2.6.1 實驗模型運轉狀態 26
2.6.2 運轉狀態之模擬 27
第3章、 主動式運轉狀態監測之方法 29
3.1 前言 29
3.2 主動式運轉狀態監測方法 30
3.3 主成份分析法(Principal Component Analysis,PCA) 32
3.3.1 PCA簡介 32
3.3.2 PCA應用於主動式運轉狀態監測 34
3.4 K均值分群法(K-Means Clustering) 36
3.4.1 K均值分群法簡介 36
3.4.2 K均值分群法用於主動式運轉狀態監測 37
第4章、 實際案例分析與討論 38
4.1 前言 38
4.2 案例設計與指標評估 38
4.2.1 案例設計 38
4.2.2 指標評估 39
4.3 主成份分析法和K均值分群法結果分析 40
4.3.1 案例一 40
4.3.2 案例二 45
4.3.3 案例三 51
4.3.4 案例四 55
4.4 結果分析與討論 61
第5章、 結論及未來展望 67
5.1 結論 67
5.2 未來展望 68
參考文獻 70
[1] Jaafar, A. (2012). Vibration Analysis and Diagnostic Guide. Iraq, Basrah: Researchgate.
[2] 彭善謙,「綜合振動信號於馬達故障診斷」,碩士論文,中原大學,2004年。
[3] N. Lashkari, H. F. Azgomi, J. Poshtan and M. Poshtan, "Asynchronous motors fault detection using ANN and fuzzy logic methods," 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA, 2016, pp. 1-5.
[4] J. Tian, C. Morillo, M. H. Azarian and M. Pecht, "Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis," IEEE Transactions on Industrial Electronics, vol. 63, no. 3, pp. 1793-1803, March 2016.
[5] 蔡有藤,陳宗傑,廖哲賢,「機械系統性能衰退預測與故障診斷之研究」,技術學刊,2012年,第121-129頁。
[6] H. Helmi and A. Forouzantabar, "Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS," IET Electric Power Applications, vol. 13, no. 5, pp. 662-669, May 2019.
[7] K. Vijay and K. Selvakumar, "Brain FMRI clustering using interaction K-means algorithm with PCA," 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, INDIA, 2015, pp. 0909-0913.
[8] J. Katkar, T. Baraskar and V. R. Mankar, "A novel approach for medical image segmentation using PCA and K-means clustering," 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, INDIA, 2015, pp. 430-435.
[9] D. Haiying, X. Kelei, Y. Lixia, G. Yaoqin and C. Ningning, "Operational conditions division of wind turbines," 2017 29th Chinese Control And Decision Conference (CCDC), Chongqing, CHINA, 2017, pp. 6983-6988.
[10] ISO 10816-1, “Mechanical vibration– evaluation of machine vibration by measurements on non-rotating parts– Part 1: General guidelines,” 1998.
[11] ISO 10816-3, “Mechanical vibration– evaluation of machine vibration by measurements on non-rotating parts– Part 3: Industrial machines with nominal power above 15 kW and nominal speeds between 120 r/min and 15000 r/min when measured in situ,” 1998.
[12] IEC 60034-14, “Mechanical vibration of certain machines with shaft heights 56mm and higher– Measurement, evaluation and limits of vibration severity,” 2007.
[13] VDI 2056, “Standards Of Evaluation For Mechanical Vibrations Of Machines,” 1964.
[14] P. Zhang, Y. Du, T. G. Habetler and B. Lu, "A Survey of Condition Monitoring and Protection Methods for Medium- Voltage Induction Motors," IEEE Transactions on Industry Applications, vol. 47, no. 1, pp. 34-46, Jan.-Feb. 2011.
[15] Singh GK, Al Kazzaz SAS, “Induction Machine Drive Condition Monitoring and Diagnostic Research- A Survey,” Electric Power System Research, vol. 64, no. 2, pp. 145–158, 2003.
[16] W. S. Gongora, H. V. D. Silva, A. Goedtel, W. F. Godoy and S. A. O. da Silva, "Neural approach for bearing fault detection in three phase induction motors," 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), Valencia, SPAIN, 2013, pp. 566-572.
[17] A. Gugaliya, G. Singh and V. N. A. Naikan, "Effective combination of motor fault diagnosis techniques," 2018 International Conference on Power, Instrumentation, Control and Computing (PICC), Thrissur, INDIA, 2018, pp. 1-5.
[18] 徐子權,「電氣與振動檢測法於馬達狀態評估之研究」,碩士論文,國立台灣科技大學,2014年。
[19] 曾思憲,「感應馬達主動型狀態估測系統之研發」,碩士論文,國立台灣科技大學,2018年。
[20] 林芝以,「運用電氣訊號於感應馬達狀態監測與故障診斷之研究」,碩士論文,台灣科技大學電機工程學系,2018年。
[21] Diego Coronado and Katharina Fischer, “Condition Monitoring of Wind Turbines: State of the Art, User Experience and Recommendation,” Fraunhofer Institute for Wind Energy and Energy System Technology IWES, January 2015.
[22] Mobius Institute. (2018). Vibration Analysis Definitions. Retrieved from https://www.mobiusinstitute.com/site2/item.asp?LinkID=2001
[23] William R. Finley, Barton J. Sauer, and Moheb Loutfi, “Motor Vibration Problems: How to Diagnose and Correct Vibration Errors,” IEEE Transactions on Industry Applications, vol. 21, no. 6, pp. 14-28, Nov.-Dec. 2015.
電子全文 電子全文(網際網路公開日期:20240808)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔