請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46878
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔡進發(Jing-Fa Tsai) | |
dc.contributor.author | Teng-Yang Chi | en |
dc.contributor.author | 紀騰揚 | zh_TW |
dc.date.accessioned | 2021-06-15T05:42:37Z | - |
dc.date.available | 2015-08-20 | |
dc.date.copyright | 2010-08-20 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-20 | |
dc.identifier.citation | 參考文獻
[1] http://www.moeaboe.gov.tw/opengovinfo/Plan/all/energy_year/main/ [2] 呂威賢,台灣風力發電史,再生能源電子,2004 [3] Raghavendra Rao Nelamane Vijayakumar, “Risk Analysis of OffShore Wind Farm”, 2007 [4] Kahn Jr., C. E., Laur, J. J. and Carrera, G. F., “A Bayesian Network for Diagnosis of Primary Bone Tumors,” Journal of Digital Imaging, Vol. 14, No. 2, pp. 56-57, 2001. [5] Romessis, C. and Mathioudakis, K., “Bayesian Network Approach for Gas Path Fault Diagnosis,” Journal of Engineering for Gas Turbines and Power, Vol. 128, No. 1, pp. 64-72, 2006. [6] AV Oppenheim, AS Willsky, S Hamid, “ Signals and systems”, 1997 [7] JW Cooley, JW Tukey, “An algorithm for the machine calculation of complex Fourier series”, Mathematics of computation, 1965 [8] http://www.ancad.com.tw/ [9] J Han, M Kamber, “Data mining: concepts and techniques”, 2006 [10] J. B. MacQueen, 'Some Methods for classification and Analysis of Multivariate Observations', Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297, 1967 [11] Alpaydin, Ethem, “Introduction To Machine Learning. Cambridge, Massachusetts: MIT Press”, p.139, 2004. [12] Nir Friedman, Dan Geiger, Moises Goldszmidt, “Bayesian Network Classifiers”, Machine Learning, 29, pp.131-163, 1997 [13] zh.wikipedia.org/zh-tw/貝氏網路 [14] 張智傑,康淵,齒輪故障之模糊類神經網路,中原大學機械工程學系碩士論文,2005 [15] 丁康,李蘶準,朱小勇,齒輪及齒輪箱故障診斷實用技術,機械工業出版社 [16] 陳長征,胡立新,周勃,費朝陽,設備振動分析與故障診斷技術,科學出版社 [17] http://tpiweb.tungpei.com.tw/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46878 | - |
dc.description.abstract | 摘要
本研究偵測風力發電機的軸承以及齒輪箱振動訊號,以進行故障診斷,並藉由實驗數據建立頻譜與故障情況之間的關係。 實驗操作上將建立齒輪軸承轉子實驗平台,用以模擬出三種齒輪故障訊號,包括齒輪不平衡、齒輪斷齒以及軸不平行等,並將時域訊號作快速傅立葉轉換取得頻譜訊號,再從中擷取頻譜特徵作為診斷依據。 擷取特徵數據後便透過K平均法以及貝氏網路別分進行分析。分析結果顯示,採用貝氏網路分析時,有著明顯優於K平均法的準確率,且貝氏網路的平均準確率高達90%以上。 | zh_TW |
dc.description.abstract | Abstract
This paper studies on monitoring vibrational signal of bearing and gearbox in wind turbine to diagnose its condition, and build up the relationship between fault and spectrum by using experimental data. To simulate three kinds of failure conditions of gears, including imbalance gear, tooth breakage and unparallel shaft, the gear-rotor system is built up for the gear fault experiment. Fast Fourier Transform will transform time domain signal into spectrum which is frequency domain signal, and extract features of spectrum as the basis of diagnosis. After features extraction, K-means algorithm and Bayesian Network are used to analyze features of spectrum. It is shown that Bayesian Network has higher precision as compared with K-means algorithm, and the average precision of Bayesian Network is up to 90 percent and above. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T05:42:37Z (GMT). No. of bitstreams: 1 ntu-99-R97525073-1.pdf: 3726349 bytes, checksum: 7049a6710840cafc8f8bd2e87656779c (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 目錄
口試委員審定書 I 摘要 II Abstract III 目錄 IV 第一章 緒論 1 1-1 研究動機 1 1-2 研究目的與方法 2 1-3 論文架構 2 第二章 研究理論 3 2-1 類比數位訊號轉換與取樣定理 3 2-2 傅立葉轉換(Fourier Transform) 4 2-3 資料分群診斷方法 7 2-3-1 分群與分類(Clustering and Classification) 7 2-3-2 K平均演算法(K-means Algorithm) 8 2-4 貝氏診斷方法 9 2-4-1 貝氏定理(Bayesian Theorem) 9 2-4-2 簡易貝氏分類器(Naïve Bayesian Classifier) 10 2-4-3 TAN (Tree-Augmented naïve Bayesian Network) 12 2-4-4 最大似然法則(Maximum Likelihood) 12 2-4-5 貝氏網路(Bayesian Network) 13 第三章 故障類型與實驗設計 14 3-1 故障類型 14 3-1-1 齒輪振動頻率 14 3-1-2 常見的齒輪故障 16 3-1-3 軸承振動頻率 18 3-1-4 常見的軸承故障 20 3-2 實驗設計 21 3-2-1 實驗設備裝置與規格 21 3-2-2 實驗平台校正 24 3-2-3 實驗平台頻率解析 26 第四章 實驗結果 33 4-1 故障訊號處理 33 4-2 資料分群診斷 39 4-2-1 K平均法分析單一轉速: 40 4-2-2 K平均法分析混合轉速: 41 4-3 貝氏網路診斷 44 4-3-1 感測器位置對分析的影響 47 4-3-2 變轉速實驗分析 51 第五章 結論與建議 55 參考文獻 56 附錄 58 | |
dc.language.iso | zh-TW | |
dc.title | 風力發電機關鍵零組件故障診斷之研究 | zh_TW |
dc.title | The study on fault diagnosis of key components in wind turbine | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 江茂雄,鐘裕亮,王俊傑 | |
dc.subject.keyword | 風力發電,故障診斷,快速傅立葉轉換,K平均法,貝氏網路, | zh_TW |
dc.subject.keyword | wind energy,fault diagnosis,Fast Fourier Transform,K-means algorithm,Bayesian Network, | en |
dc.relation.page | 63 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-20 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-99-1.pdf 目前未授權公開取用 | 3.64 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。