請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23439
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊宏智(Hong-Tsu Young) | |
dc.contributor.author | Wei-Yen Lin | en |
dc.contributor.author | 林威延 | zh_TW |
dc.date.accessioned | 2021-06-08T05:01:40Z | - |
dc.date.copyright | 2010-11-15 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-10-14 | |
dc.identifier.citation | [1] Paresh Girdhar, “Practical Machinery Vibration Analysis and Predictive Maintenance”, Newnes, Amsterdam, 2004.
[2] N. E. Huang, Z. Shen, S. R. Long, et al., “The Empirical Mode Decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proc. Roy. Soc. London A, Vol. 454 , pp. 903-995, 1998. [3] N.E. Huang, Z. Shen and S.R. Long, “A new view of nonlinear water waves: the Hilbert spectrum”, Annual Reviews of Fluid Mechanics 3 417–457, 1999 [4] N.E. Huang, M.C. Wu, S.R. Long, S.S. Shen, W. Qu, P. Gloersen, K. L. Fan, ”A confidence limit for the empirical mode decomposition and Hilbert spectral analysis”, Proc. R. Soc. London Ser. A459:2317-2345, 2003 [5] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: A Noise-Assisted data analysis method”, Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp. 1-41, 2009 [6] 張復瑜, 王嘉, “MSE演算法探討與改良以導入迴轉機械領域建立車削特性與迴轉機械品質即時檢測系統”, 碩士論文, 2010. [7] P. Tse, “Neural Networks Based Robust Machine Fault Diagnostic & Life Span Predicting System”, Ph. D. Thesis, The University of Sussex, United Kingdom, 1998. [8] J. Park, “Practical Data Acquisition for Instrumentation and Control Systems”, Newnes, Oxford, 2003. [9] J. S. Wilson, “Sensor Technology Handbook”, Newnes, Oxford, 2005. [10] Thomas F. Hansen, “Accelerometer Mounting Techniques”, B&K Web Course, June 2007. [11] “Measurements Manual”, April 2003 Edition; Part Number 322661B-01, National Instruments, Austin, TX, 2003. [12] Paresh Girdhar, “Practical Machinery Vibration Analysis and Predictive Maintenance”, Newnes, Amsterdam, 2004. [13] 潘敏俊、朱效賢, “包絡譜分析於軸承故障診斷之探討暨工程應用”, 碩士論文,國立中央大學機械工程研究所,2005。 [14] Muszynska A, 1986, “Whirl and Whip — Rotor and Bearing Stability Problems”, Journal of Sound and Vibration, Vol. 110, No. 3, pp. 443-462 [15] Muszynska A., Bently D. E., Franklin W. D., Grant J., Goldman P., 1993, “Applications of Sweep Frequency Rotating Force Perturbation Methodolory in Rotating Machinery for Dynamic Stiffness Identification”, Transaction of the ASME, Vol. 115, pp.266-271 [16] 林若宛, 董必正, “油膜軸承迴轉機之鑑別實驗”, 碩士論文,國立中央大學機械工程研究所,2000。 [17] Jose A Mendez-Adriani, “Consideration on the field balancing of the overhung rigid rotors”, shock and vibration digest, v37 n3 179-187, 2004 [18] A. S. Sekhar and B. S. Prabhu, “Effects of Coupling Misalignment on Vibration of Rotating Machines”, Journal of Sound and Vibration, 185, pp. 655–671, 1995. [19] SpectraQuest Inc., “Interesting Rotor Dynamics Observations on Oil Whirl and Whip”, April, 2006 [20] J. C. Mitchell, “Introduction to Machinery Analysis and Monitoring”, PennWell, Tulsa, Okla, 1993. [21] C. J. Li and S. M. Wu, “On-line Detection of Localized Defects in Bearings by Pattern Recognition Analysis”, ASME J. Eng. Ind., 111, pp. 331–336, 1989. [22] J. Sandy, “Monitoring and Diagnostics for Rolling Element Bearings”, Journal of Sound Vibration, 22, No. 6, pp. 16–20, 1988. [23] Peter W. Tse, Y. H. Peng and Richard Yam, “Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis-Their Effectiveness and Flexibilities”, Journal of Vibration and Acoustics, Vol. 123, pp. 303-311, July 2001 [24] Yonghong Peng, “Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage”, Journal of Manufacturing Science and Engineering, Transaction of the ASME, Vol. 128, pp. 154-166, February 2006. [25] Ruqiang Yan, “Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring”, IEEE Transactions on Instrumentation and Measurement, Vol. 55, NO. 6, December 2006. [26] Z.K. Peng, Peter W. Tse and F.L. Chu, “A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing”, Mechanical Systems and Signal Processing 19 974–988, 2005 [27] Dejie Yu, Junsheng Cheng and Yu Yang, “Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings”, Mechanical Systems and Signal Processing 19 259–270, 2005 [28] Cheng Junsheng, Yu Dejie and Yang Yu, “A fault diagnosis approach for roller bearings based on EMD method and AR model”, Mechanical Systems and Signal Processing 20 350–362, 2006 [29] Yang Yu, YuDejie and Cheng Junsheng, “A roller bearing fault diagnosis method based on EMD energy entropy and ANN”, Journal of Sound and Vibration 294 269–277, 2006 [30] V.K. Rai and A.R. Mohanty, “Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform”, Mechanical Systems and Signal Processing 21 2607–2615, 2007 [31] Wei-Yen Lin, Li-Chang Chuang, Hong-Tsu Young, “Condition-based Shaft Faults Diagnosis with EMD Method”, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, Accepted & To-Be-Published [32] S.M. Pincus, “ Approximate Entropy as a Measure of System Complexity“, PNAS, 88:2297-2301, 1991. [33] J.S. Richman and J.R. Moorman, “ Physiological time-series analysis using approximate entropy and sample entropy“, Am J Physiol Heart Circ Physiol 278:H2039-H2049, 2000. [34] Costa M., Goldberger A.L., Peng C.-K., “Multi-scale Entropy Analysis of Physiologic Time Series”, Phys Rev Lett, 89:062102., 2002 [35] Costa M., Goldberger A.L., Peng C.-K. Multiscale entropy to distinguish between physiologic and synthetic RR time series. Computers in Cardiology 2002;29:137-140 [36] Costa M, Peng C.-K , Goldberger A.L and Hausdorff J.M. ” Multiscale entropy analysis of human gait dynamics” Physica A,330:53-60, 2003. [37] Tetsuya Takahashi, Raymond Y. Cho, Tomoyuki Mizuno. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: A multiscale entropy analysis. NeuroImage 51 (2010) 173–182. [38] Xiaoxu Kang, Xiaofeng Jia,. Multiscale Entropy Analysis of EEG for Assessment of Post-Cardiac Arrest Neurological Recovery Under Hypothermia in Rats. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 4, APRIL 2009 [39] Yu-Hsiang Pan, Wei-Yen Lin, Yung-Hung Wang, Kuo-Tien Lee, “Computing Multi-scale Entropy With Orthogonal Range Search”, Journal of Materials Science and Technology, Accepted & To-Be-Published [40] Detlev J.Hoch, 數位式競爭-全球軟體公司的致勝策略, ISBN:9576217199 [41] Jun-Lin Lin, Julie Yu-Chih Liu, Chih-Wen Li, Li-Feng Tsai, Hsin-Yi Chung, “Expert Systems with Applications”, Expert Systems with Applications, VOL 37 P. 7200–7204, APRIL 2010. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23439 | - |
dc.description.abstract | 為了提升全球競爭力,提昇產品品質、降低生產成本、縮短製造及維修時間為目前工具機產業必須採取的競爭策略,機械在運作時會產生震動與噪音,並且可以透過監測此數值達到非破壞檢測與監控。但往往受限於成本或無適當量測工具,只能採取試誤法為檢測的方式,不僅浪費時間、材料及人力成本,更降低生產力。本研究透過人工製作以及退修主軸蒐集等兩種方式,建立主軸常見的損壞模型,除了傳統的傅立葉轉換之外,使用了經驗模態分解法和多尺度熵等三種訊號處理方法,開發特徵擷取以及比對演算法,建立主軸損壞辨識系統。由於機械震動通常為非穩態且非線性的訊號,傳統傅立葉轉換有其限制,本研究利用經驗模態分解法將原始訊號拆解成各個內部模態函數,並透過過零點速度以及能量分佈來表示各種模型的訊號特徵,對於大部分的模型分析有顯著效果,且研究中發現,若是為合格主軸,會產生四個目標內部模態函數;若是為組裝瑕疵主軸,例如不對心、潤滑油過多過少、預壓過大過小,會產生五個目標內部模態函數;若是是結構損壞主軸,會產生六個目標內部模態函數;多尺度熵則是計算訊號在各尺度下的亂度值,此方法亦在此研究中驗證針對某些特徵模型有非常佳的辨識效果。
本研究於最後提出快速軟體開發方法,並且利用此方式開發製程管制系統 (Manufacturing Execution System, MES),製程管制系統是指生產現場電子化與製程之控管,系統以即時的方式,收集生產製程中各種資訊,供生產與管理者等參考,除了幫助生產管理者,管控生產製程外,更重要的是透過統計分析,找出每一種主軸最佳的精度參數,搭配本研究開發的損壞辨識系統,於台中工具機廠商驗證,提升其主軸品質。 | zh_TW |
dc.description.abstract | Aiming at reducing cost and time of repair, condition-based shaft faults diagnosis is considered an efficient strategy for machine tool community. While the shaft with faults is operating, its vibration signals normally indicate nonlinear and non-stationary characteristics with its Fourier-based approaches shown limitations for handling this kind of signals. The methodology proposed in this research is to extract the features from shaft faults related vibration signals, from which the corresponding fault condition is then effectively identified. Besides Fourier Transform, two new algorithms are used to extract the feature of signals, empirical mode decomposition (EMD) and multi-scale entropy (MSE). With an incorporation of EMD method, the model applied in this research embraces some characteristics, like zero-crossing rate and energy, of intrinsic mode functions (IMFs) to represent the feature of the shaft condition. The other method called MSE is used to calculate the entropy of multi-scale of the signal. The curve of MSE can be used to identify some defect model of shafts clearly. Fourier-based, EMD-based and MSE-based methods were implemented to develop a diagnosis system in this research. In the buildup stage a knowledgeware is created from the database compiled from the existing defect models. Finally, the Manufacturing Execution System (MES), conventionally called in the production field, is developed with diagnosis system. The system will collect various kinds of information during production process in real-time, and provide them to supervisors and the management in production line for their references. MES and fault diagnosis system are both implemented in a machine tool manufacturing company to validate its capacity. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:01:40Z (GMT). No. of bitstreams: 1 ntu-99-D93522009-1.pdf: 4740460 bytes, checksum: b07582766e100df784d3c14ca71e86ad (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 致謝 I
摘要 II Abstract III 目錄 IV 1. 緒論 14 1.1 研究動機與目的 14 1.2 研究方法 16 1.3 研究架構 17 2. 資料擷取模組與實驗架構 18 2.1 感測器特性簡介 19 2.2 資料擷取卡特性簡介 22 2.3 軟體系統 23 2.4 實驗規劃 24 2.4.1 實驗條件 25 2.4.2 樣本震動值 26 2.4.3 實驗分析流程 27 3. 以傅立葉轉換為基礎之特徵擷取及比對演算法 29 3.1 傅立葉頻譜於主軸相關研究 29 3.2 演算法介紹 33 3.2.1 傅立葉轉換理論介紹 33 3.2.2 傅立葉特徵頻率能量分佈圖 34 3.2.3 傅立葉特徵向量 36 3.2.4 傅立葉特徵向量相關度指標 37 3.3 各損壞模型的傅立葉特徵頻率能量分佈圖 37 3.4 傅立葉為基礎的損壞辨識系統 41 3.5 小結 42 4. 以經驗模態拆解法為基礎之特徵擷取及比對演算法 43 4.1 經驗模態拆解法介紹 43 4.2 方法論 [31] 45 4.2.1 Empirical Mode Decomposition (EMD) 演算法簡介 45 4.2.2 Intrinsic Mode Function 特徵值 47 4.2.3 階次能量圖 (Order-Energy Plot) [31] 50 4.2.4 特徵向量 (Feature Vectors) [31] 50 4.2.5 相關度指標 [31] 50 4.3 各損壞模型的階次頻率圖與特徵向量 [31] 51 4.4 EMD為基礎的損壞辨識系統 57 4.4.1 各損壞模型EMD 特徵向量相關度 57 4.4.2 系統可靠度測試 58 4.4.3 比對診斷可靠度測試 58 4.5 小結 61 5. 以多尺度熵為基礎之特徵擷取及比對演算法 62 5.1 熵 (Entropy) 簡介 62 5.2 Multi-scale Entropy 演算法 63 5.2.1 MSE 演算法介紹 63 5.2.2 快速 MSE 演算法 [39] 65 5.2.3 MSE 分佈圖特徵與相關度指標 68 5.2.4 Pink Noise v.s. White Noise 69 5.3 MSE 實驗分析結果 71 5.3.1 各損壞模型分析結果 71 5.3.2 結果比較 76 5.3.3 相似度矩陣 76 5.4 小結 77 6. 軟體開發方法與系統架構 79 6.1 軟體工程簡介 79 6.1.1 軟體開發方法 79 6.1.2 系統架構 81 6.1.3 極限編程 (Extremely Programming) 82 6.2 物件導向技術 (Object-Oriented Technique,OOP) 83 6.3 關連式資料庫管理系統 84 6.3.1 關聯式資料庫的原理 85 6.3.2 正規化理論 86 6.3.3 關聯式運算 86 6.4 本研究系統開發方法與架構 87 6.4.1 軟體系統架構 87 6.4.2 軟體開發方法 94 6.5 小結 96 7. 製程管制系統與損壞辨識系統開發 97 7.1 發展動機 97 7.2 系統設計 99 7.2.1 製程管制系統使用案例、資料庫 ER Model 以及靜態物件結構圖 99 7.2.2 損壞辨識系統資料庫 ER Model 以及靜態物件結構圖 104 7.3 系統操作頁面 105 7.3.1 製程管控模組 105 7.3.2 生產報表模組 107 7.3.3 統計分析模組 108 7.3.4 損壞辨識系統 108 7.4 小結 109 8. 結論 111 8.1 結論 111 8.2 未來展望 113 參考文獻 117 | |
dc.language.iso | zh-TW | |
dc.title | 工具機主軸製程管制系統與損壞辨識系統開發 | zh_TW |
dc.title | Development of Manufacture Execution System & Fault Diagnosis System | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 蘇侃,李貫銘,陳湘鳳,林昌進,柯志隆 | |
dc.subject.keyword | 經驗模態分解法,內部模態函數,過零點速度,多尺度熵,主軸,損壞辨識, | zh_TW |
dc.subject.keyword | EMD,IMF,Zero-Crossing Rate,MSE,MES,Shaft,Diagnosis, | en |
dc.relation.page | 119 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2010-10-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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 | 4.63 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。