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研究生:陳宇軒
研究生(外文):Yu-Hsuan Chen
論文名稱:應用於顫振辨識之特徵選擇與分類方法之研究
論文名稱(外文):A study of feature selection and classification methods for chatter identification models
指導教授:劉建豪劉建豪引用關係
口試委員:施文彬蔡孟勳蔡曜陽
口試日期:2019-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:機械工程學研究所
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:2019
畢業學年度:107
語文別:英文
論文頁數:109
中文關鍵詞:顫振小波包轉換希爾伯特-黃轉換最近鄰居法支持向量機
DOI:10.6342/NTU201903636
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維持高產率對於銑削加工的效率而言十分重要。顫振是加工時發生的一種自激式振動,在實務中限制了產率。過去的研究提出了許多顫振偵測的方法,利用各種訊號處理的方法如快速傅立葉轉換(FFT),小波包轉換(WPT),及希爾伯特-黃轉換(HHT)。許多資料分類演算法也被應用於顫振偵測。雖然顫振偵測的領域已有許多文獻,我們仍不清楚何種方法可以達到較佳的正確率與偵測速率。
在本研究中,我們將測試多種訊號處理方法以及資料分類的演算法,使用的資料集中包含各種主軸轉速及切深。我們結合多種訊號處理方法及分類演算法,開發了一個顫振辨識平台以建立分類模型並評估其性能。資料分類方法包含了固定的閾值,最近鄰居法(k-NN),單純貝氏分類器,支持向量機(SVM),局部密度因子(LOF),以及類神經網路。以分類精準度而言,結果顯示最近鄰居法搭配小波包轉換及希爾伯特-黃轉換是最佳的方法,誤判率僅2.2%。
Maintaining high production yield is important for efficiency in the milling process. Chatter is a type of self-excited vibration that can occur during machining, and limits the production yield in practice. In the past, many chatter detection methods were proposed using different signal processing methods such as Fast Fourier transform (FFT), wavelet packet transform (WPT), and Hilbert-Huang transform (HHT). Several classification methods were also applied in chatter detection. Despite the large amount of researches regarding chatter detection, it is unclear which of these proposed methods are better in terms of accuracy and detection speed.
In this research, we test the signal processing methods and classification algorithms against the entire dataset, with a wide range of spindle speeds and depth of cuts. A chatter identification platform is developed to train models and evaluate their performance, using combinations of signal processing methods and classification algorithms. The classification methods include numerical threshold, k-nearest neighbors (K-NN), Naïve Bayes, support vector machine (SVM), local outlier factor (LOF), and artificial neural network. K-NN proves to be the optimal method when using WPT and HHT for signal processing, with an error rate of 2.2%.
1. Introduction ........................................................................................................... 12
1.1 High-speed milling and chatter ........................................................................ 12
1.2 Chatter detection .............................................................................................. 14
1.3 Aim of this research ......................................................................................... 16
1.4 Structure of the thesis ...................................................................................... 18
2. Signal processing methods and feature extraction ............................................. 19
2.1 Fast Fourier transform (FFT) ........................................................................... 19
2.2 Wavelet packet transform (WPT) .................................................................... 22
2.3 Autocorrelation coefficients ............................................................................ 26
2.4 Hilbert-Huang transform (HHT) ...................................................................... 29
3. Classification algorithms ...................................................................................... 33
3.1 Numerical threshold ......................................................................................... 33
3.2 Naïve Bayes ..................................................................................................... 34
3.3 Local outlier factor (LOF) ............................................................................... 35
3.4 Support vector machine (SVM) ....................................................................... 36
3.5 K-nearest neighbor (k-NN) .............................................................................. 38
3.6 Artificial neural network (ANN) ..................................................................... 39
4. Implementation...................................................................................................... 40
4.1 Architecture of the data analysis and model training platform ............................... 40
4.2 Implementation details ..................................................................................... 42
4.2.1 Zero-padding before FFT ............................................................................... 42
4.2.2 Computing autocorrelation coefficients ......................................................... 43
4.2.3 Peak finding.................................................................................................... 45
4.3 Validation ......................................................................................................... 47
5. Results and discussion ........................................................................................... 48
5.1 Data collection and labeling.................................................................................. 48
5.2 Comparisons of classification algorithms ............................................................. 49
5.3 Parameters optimizations ................................................................................. 54
5.3.1 Fast Fourier transform (FFT) ......................................................................... 55
5.3.2 Wavelet packet transform (WPT) .................................................................. 63
5.3.3 Autocorrelation coefficients ........................................................................... 69
5.3.4 Hilbert-Huang transform (HHT) .................................................................... 77
5.3.5 Frequency spectrum (with artificial neural network) ..................................... 79
5.4 Comparison of features ......................................................................................... 80
5.5 Effect of window size ........................................................................................... 83
5.5.1 Error rates ....................................................................................................... 83
5.5.2 Detection speed .............................................................................................. 85
6. Conclusions and future work ............................................................................... 88
References...................................................................................................................... 89
Appendix A. List of cutting conditions in the dataset ............................................. 100
Appendix B. Model training and validation results ................................................ 102
[1]Atanas Ivanov, Rebecca Leese, and Alexandre Spieser, Micromanufacturing Engineering and Technology (Second Edition), Micro and Nano Technologies, 2015.
[2]Ronald Faassen, "Chatter Prediction and Control for High-Speed Milling Modelling and Experiments," Ph. D. thesis, 2007.
[3]Guillem Quintana and Joaquim Ciurana, "Chatter in machining processes: A review," International Journal of Machine Tools & Manufacture, pp. 363-376, 2011.
[4]Erol Turkes, Sezan Orak, Suleyman Neseli, and Suleyman Yaldiz, "Linear analysis of chatter vibration and stability for orthogonal cutting in turning," Int. Journal of Refractory Metals and Hard Materials, pp. 163-169, 2011.
[5]J. Tlusty and F. Ismail, "Basic Non-Linearity in Machining Chatter," CIRP Annals, vol. 30, no.1, pp. 299-304, 1981.
[6]J. Tlusty, W. Zaton, and F. Ismail, "Stability Lobes in Milling," CIRP Annals, vol. 32, no. 1, pp. 309-313, 1983.
[7]Y. Altintaş and E. Budak, "Analytical Prediction of Stability Lobes in Milling," CIRP Annals, vol. 44, no. 1, pp. 357-362, 1995.
[8]R. P. H. Faassen, N. V. D. Wouw, J. Oosterling, and H. Nijmeijer, "Prediction of regenerative chatter by modelling and analysis of high-speed milling," International Journal of Machine Tools and Manufacture, vol. 43, no. 14, pp. 1437-1446, 2003.
[9]E. Kuljanic, G.Totis, and M. Sortino, "Vibrations and Chatter in Machining: State of the Art and New Approaches," in Advanced Manufacturing Systems and Technology, 2008.
[10]E. Solis, C. Peres, J. Jiménez, J. Alique, and J. Monje, "A new analytical–experimental method for the identification of stability lobes in high-speed milling," International Journal of Machine Tools and Manufacture, vol. 44, no. 15, pp. 1591-1597, 2004.
[11]S. D. Merdol and Y. Altintas, "Multi Frequency Solution of Chatter Stability for Low Immersion Milling," Journal of Manufacturing Science and Engineering, vol. 126, no. 3, pp. 459-466, 2004.
[12]T. Insperger and G. Stépán, "Semi-discretization method for delayed systems," International Journal for Numerical Methods in Engineering, vol. 55, no. 5, pp. 503-518, 2002.
[13]J. Muñoa, M. Zatarain, Z. Dombovari, and Y. Yang, "Effect of Mode Interaction on Stability of Milling Processes," in 12th CIRP Conference on Modelling of Machining Operations, San Sebastian, Spain, 2009.
[14]Z. Dombóvári, "Overview of stability analysis in machining processes," Technical report, 2008.
[15]Y. Altintas, "Analytical prediction of three dimensional chatter stability in milling," JSME Int J C-Mech Syst, vol. 44, no. 3, pp. 717-723, 2001.
[16]Y. Altintas, E. Shamoto, P. Lee, and E. Budak, "Analytical prediction of stability lobes in ball end milling," J Manuf Sci E-T ASME, vol. 121, no. 4, pp. 586-592, 1999.
[17]A. Tang and Z. Liu, "Three-dimensional stability lobe and maximum material removal rate in end milling of thin-walled plate," Int J Adv Manuf Technol, vol. 43, no. 1, pp. 33-39, 2009.
[18]C. Toh, "Vibration analysis in high speed rough and finish milling hardened steel," Journal of Sound and Vibration, vol. 278, no. 1-2, pp. 101-115, 2004.
[19]Z. Han, H. Jin, M. Li, and H. Fu, "An open modular architecture controller based online chatter suppression system for CNC milling," Mathematical Problems in Engineering,, p. 13, 2015.
[20]M. C. Yoon and D. H. Chin, "Cutting force monitoring in the endmilling operation for chatter detection," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 219, no. 6, pp. 455-465, 2005.
[21]Z. Yao, D. Mei, and Z. Chen, "On-line chatter detection and identification based on wavelet and support vector machine," Journal of Materials Processing Technology, vol. 210, no. 5, pp. 713-719, 2010.
[22]Y. Sun and Z. Xiong, "An Optimal Weighted Wavelet Packet Entropy Method With Application to Real-Time Chatter Detection," IEEE/ASME Transactions on Mechatronics, vol. 21, no. 4, pp. 2004-2014, 2016.
[23]Y. Sun, C. Zhuang, and Z. Xiong, "Real-time chatter detection using the weighted wavelet packet entropy," in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2014.
[24]G. G. Yen and K. C. Lin, "Wavelet packet feature extraction for vibration monitoring," IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 650-667, 2000.
[25]Melih C. Yesilli, Firas A. Khasawneh, and Andreas Otto, "On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition," arXiv, 2019.
[26]B. S. Berger I. Minis, J. Harley, M. Rokni, and M. Paradopoulos, "Wavelet based cutting state identification," Journal of Sound and Vibration, vol. 213, no. 5, pp. 813-827, 1998.
[27]A. Ordaz-Moreno, R. de Jesus Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, and A. Garcia-Perez, "Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation," IEEE Transactions on Industrial Electronics, vol. 55, no. 5, pp. 2193-2202, 2008.
[28]E. Soliman and F. Ismail, "Chatter detection by monitoring spindle drive current," The International Journal of Advanced Manufacturing Technology, vol. 13, no. 1, pp. 27-34, 1997.
[29]Deniz Aslan and Yusuf Altintas, "On-line chatter detection in milling using drive motor current commands extracted from CNC," International Journal of Machine Tools and Manufacture, vol. 132, 2018.
[30]R. Du, M. Elbestawi, and B. Ullagaddi, "Chatter detection in milling based on the probability distribution of cutting force signal," Mechanical Systems and Signal Processing, vol. 6, no. 4, pp. 345-362, 1992.
[31]S. Tangjitsitcharoen, "In-process monitoring and detection of chip formation and chatter for CNC turning," Journal of Materials Processing Technology, vol. 209, no. 10, pp. 4682-4688, 2009.
[32]H. Cao, Y. Lei, and Z. He, "Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform," International Journal of Machine Tools and Manufacture, vol. 69, pp. 11-19, 2013.
[33]Yongjian Ji, Xibin Wang, Zhibing Liu, Hongjun Wang, Li Jiao, Dongqian Wang, and Shouyang Leng, "Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation," Journal of Sound and Vibration, vol. 433, pp. 138-159, 2018.
[34]Wei Peng, Zhongju Hu, Li Yuan, and Pingyu Zhu, "Chatter identification using HHT for boring process," in International Conference on Optical Instruments and Technology, 2013.
[35]R. Q. Yang and X.Gao, "Hilbert-Huang transform-based vibration signal analysis for machine health monitoring," IEEE Trans. Instrum. Meas., vol. 55, no. 6, pp. 2320-2329, 2006.
[36]Tang, J. P., Chiou, D. J., Chen, C. W., Chiang, W. L., Hsu, W. K., Chen, C. Y., and Liu, T. Y., "A case study of damage detection in benchmark buildings using a hilbert-huang transform-based method," Journal of Vibration and Control, vol. 17, no. 4, pp. 623-636, 2011.
[37]Michał Szydłowski and Bartosz Powałka, "Chatter detection algorithm based on machine vision," Int J Adv Manuf Technol, vol. 65, pp. 517-528, 2011.
[38]Zhenga H, Kongb LX, and Nahavandia S, "Automatic inspection of metallic surface defects using genetic algorithms," Journal of Materials Processing Technology, vol. 125, pp. 427-433, 2007.
[39]Lee BY and Tarng YS, "Surface roughness inspection by computer vision in turning operations," Int J Mach Tool Manuf 41, pp. 1251-1263, 2007.
[40]Ching-Chih Wei, Meng-Kun Liu, and Guo-Hua Huang, "Chatter Identification of Face Milling Operation via Time-Frequency and Fourier Analysis," International Journal of Automation and Smart Technology, pp. 25-36, 2016.
[41]X. Q. Li, Y. S. Wong, and A. Y. C. Nee, "A Comprehensive Identification of Tool Failure and Chatter Using a Parallel Multi-ART2 Neural Network," Journal of Manufacturing Science and Engineering, vol. 120, no. 2, p. 433, 1998.
[42]M. Lamraoui, M. Barakat, M. Thomas, and M. E. Badaoui, "Chatter detection in milling machines by neural network classification and feature selection," Journal of Vibration and Control, vol. 21, no. 7, pp. 1251-1266, 2013.
[43]J. Hino, S. Okubo, and T. Yoshimura, "Chatter Prediction in End Milling by FNN Model with Pruning," JSME International Journal Series C, vol. 49, no. 3, pp. 742-749, 2006.
[44]Yang Y, Yu D, and Cheng J, "A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM," Measurement, no. 40, vol. 9–10, pp. 943-950, 2007.
[45]Tan F, Yin M, Wang L, and Yin G, "Spindle thermal error robust modeling using LASSO and LS-SVM," Int J Adv Manuf Technol, no. 94, vol. 5, pp. 2861-2874, 2018.
[46]Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, and Sen R, "Tool condition monitoring by SVM classification of machined surface images in turning," Int J Adv Manuf Technol, vol. 83, no. 9, pp. 1487-1502, 2016.
[47]Y.-C. Yao, Real-time Chatter Detection, Analysis and Suppression Using in Intelligent Spindles Based on One-class Support Vector Machine and Local Outlier Factor (Master thesis), 2018.
[48]Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander, "LOF: Identifying Density-Based Local Outliers," in Proc. ACM SIGMOD 2000 Int. Conf. On Management of Data, Dalles, TX, 2000.
[49]Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, "A Practical Guide to Support Vector Classification," Techinical report, 2003.
[50]S. Tangjitsitcharoen, "Analysis of Chatter in Ball End Milling by Wavelet Transform," International Journal of Industrial and Manufacturing Engineering, vol. 6, no. 11, pp. 2438-2444, 2012.
[51]R. Brancati, E. Rocca, S. Savino, and F. Farroni, "Gear Rattle Analysis Based on Wavelet Signal Decomposition," in Proceedings of the ASME 2012 11th Biennial Conference On Engineering Systems Design And Analysis, 2012.
[52]Sami Ekici, Selcuk Yildirim, and Mustafa Poyraz, "Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition," Expert Systems with Applications, Volume 34, Issue 4, 2008.
[53]Shaoke Wan, Xiaohu Li, Wei Chen, and Jun Hong, "Investigation on milling chatter identification at early stage with variance ratio and Hilbert–Huang transform," The International Journal of Advanced Manufacturing Technology, Volume 95, Issue 9–12, pp. 3563-3573, 2017.
[54]Nayana Gandhi, "FFT based evaluation of cutting forces and chatter vibrations in turning by varying speed, feed, depth of cut and rake angle.," in GIT-Journal of Engineering and Technology , 2012.
[55]Haosheng Li, Bin Wu, and Hubert Kratz, "FFT and Wavelet-Based Analysis of the Influence of Machine Vibrations on Hard Turned Surface Topographies," Tsinghua Science & Technology, vol. 12, no. 4, pp. 441-446, 2007.
[56]F.B.J.W.M. Hendriks, "Chatter detection in high-speed milling," Technical report, 2005.
[57]L.R. Soares, H.M. de Oliveira, R.J.S. Cintra, and R.M. Campello de Souza, "Fourier Eigenfunctions, Uncertainty Gabor PrincipleAnd Isoresolution Wavelets," in XX Simpósio Brasileiro de Telecomunicações, Rio de Janeiro, 2003.
[58]Introduction to Wavelet Families (MATLAB online documentation).
[59]F. Safara, S. Doraisamy, A. Azman, A. Jantan, and S. Ranga, "Wavelet Packet Entropy for Heart Murmurs Classification," Advances in Bioinformatics, pp. 1-6, 2012.
[60]Eiji Kondo, US 9285797 B2 (U.S. Patent), 2016.
[61]S. D. Merdol and Y. Altintas, "Multi Frequency Solution of Chatter Stability for Low Immersion Milling," Journal of Manufacturing Science and Engineering, vol. 126, no. 3, pp. 459-466, 2004.
[62]Tamas Insperger and Gabor Stepan, "Semi-discretization method for delayed systems," International Journal for Numerical Methods in Engineering, vol. 55, no. 5, pp. 503-518, 2002.
[63]Norden E. Huang and Zhaohua Wu, "A review on Hilbert‐Huang transform: Method and its applications to geophysical studies," Reviews of Geophysics, Volume 46, Issue 2, 2008.
[64]Norden E Huang and Samuel S P Shen, Hilbert-Huang Transform and Its Applications, World Scientific, 2005.
[65]Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H. Liu, The empirical mode decomposition and theHilbert spectrum for nonlinear andnon-stationary time series analysis, Royal Society, 1996.
[66]Mathias Johansson, "The Hilbert transform," Technical report.
[67]Timothy J. Ulrich, "Envelope Calculation from the Hilbert Transform," Techinical report, 2006.
[68]Jason D. M. Rennie, Lawrence Shih, Jaime Teevante, and David R. Karger, "Tackling the Poor Assumptions of Naive Bayes Text Classifiers," in Proceedings of the Twentieth International Conference on Machine Learning, 2003.
[69]Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 2009.
[70]Kamal Nigam, Andrew McCallum, Sebastian Thrun, and Tom Mitchell, "Learning to classify text from labeled and unlabeled documents," in Association for the Advancement of Artificial Intelligence, 1998.
[71]Schubert, E.; Zimek, A.; Kriegel, H. -P., "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection," Data Mining and Knowledge Discovery, 2012.
[72]Yung-Chen Yao, Yu-Hsuan Chen, Chien-Hao Liu, Wen-Pin Shih, "Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm," The International Journal of Advanced Manufacturing Technology, pp. 1-13, 2019.
[73]N.Y. Deng, Y.J. Tian, and C.H. Zhang, Support Vector Machines: Algorithms and Extensions, CRC Press, 2012.
[74]N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other Kernel based Learning Methods, Cambridge University Press, 2000.
[75]Chen Bing, Yang Ji, Zhao Ju, and Ren Jingbo, "Milling Chatter Prediction Based on the Information Entropy and Support Vector Machine," in International Industrial Informatics and Computer Engineering Conference, 2015.
[76]G. S. Chen and Q. Z. Zheng, "Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination," The International Journal of Advanced Manufacturing Technology, Volume 95, Issue 1–4, pp. 775-784, 2018.
[77]Yongqing Wang, Qile Bo, Haibo Liu, Lei Hu, and Hao Zhang, "Mirror milling chatter identification using Q-factor and SVM," The International Journal of Advanced Manufacturing Technology, vol. 98, issue 5–8, pp. 1163-1177, 2018.
[78]R. Berwick and Village Idiot, "An Idiot’s guide to Support vector machines (SVMs)," Technical report.
[79]Tristan Fletcher, "Support Vector Machines Explained," Technical report.
[80]Altman, N. S., "An introduction to kernel and nearest-neighbor nonparametric regression," The American Statistician, pp. 175-185, 1992.
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