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Application of constrained independent component analysis and empirical mode decomposition to diagnose synchronous multiple bearing faults
|關鍵字:||約束獨立成分分析;經驗模態分解法;同時複合故障;支持向量機;Constrained independent component analysis;Empirical mode decomposition;Concurrent multiple bearing fault;Support vector machine||引用:|| N. E. Huang, Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, 'The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,' Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, Vol. 454, pp. 903-995, 1998.  Z. Wu and N. E. Huang, 'Ensemble empirical mode decomposition: a noise assisted data analysis method,' Advances in Adaptive Data Analysis, Vol. 1,pp. 1-41, 2009.  N. Tandon and A. Choudhury, 'A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,' Tribology International, Vol. 32, pp. 469-480, 1999.  Z. Kiral and H. Karagülle, 'Simulation and analysis of vibration signals generated by rolling element bearing with defects,' Tribology International, Vol. 36, pp. 667-678, 2003.  朱效賢,'包絡譜分析於軸承故障診斷之探討暨工程應用,'中央大學機械工程學系碩士論文,2005.  D. Yu, Y. Yang and J. Cheng, 'A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,' Measurement, Vol. 69, pp. 114-124, 2007.  J. S. Richman and J. R. Moorman, 'Physiological time-series analysis using approximate entropy and sample entropy,' American Journal of Physiology-Heart and Circulatory Physiology, Vol. 278, pp. H2039-H2049, 2000.  M. Costa, A. L. Goldberger and C. K. Peng, 'Multi-scale Entropy Analysis of Complex Physiologic Time Series,' Physical Review Letters, Vol. 89, No. 6, pp.068102-1-068102-4, 2002.  M. Costa, A. L. Goldberger and C. K. Peng, 'Multi-scale Entropy Analysis of Biological Signals,' Physical Review E, Vol. 89, pp. 021906-1-021906-18, 2005.  P. Comon 'Independent component analysis: a new concept?,' Signal Process., Vol. 36, pp. 287-314, 1994.  A. Bell, T. Sejnowski 'An information-maximization approach to blind separation and blind deconvolution,' Neurocomputing , Vol. 7, pp. 1129-1159, 1995.  W. Lu, J.C. Rajapakse 'Constrained independent component analysis,' Advances in Neural Information Processing Systems 13 , MIT Press, Cambridge, pp. 570-576, 2000.  W. Lu, J.C. Rajapakse 'ICA with Reference,' Neurocomputing , Vol.69, pp. 2244-2257, 2006.  Zhiyang Wang, Jin Chen,Guangming Dong and Yu Zhou 'Constrained independent component analysis and its application to machine fault diagnosis,' Mechanical Systems and Signal Processing, Vol. 25,pp. 2501-2512, 2011.  Tangfeng Yang, Yu Guo,Xing Wu, Jing Na and Rong-FongFung 'Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings,' Mechanical Systems and Signal Processing, 2017  G. Madzarov, D. Gjorgjevikj, and I. Chorbev, 'A Multi-class SVM Classifier Utilizing Binary Decision Tree,' Informatica, Vol. 33, pp.233-241, 2009.  Y. Yang, D. J. Yu, and J. S. Cheng, 'A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,' Measurement, Vol. 40, pp. 943-950, 2007.  O. R. Seryasat, M. A. shoorehdeli, F. Honarvar, A. Rahmani, and J. Haddadnia, 'Multi-fault diagnosis of ball bearing using intrinsic mode functions, Hilbert marginal spectrum and multi-class support vector machine,' International Conference on Mechanical and Electronics Engineering, Vol. 2, pp. 145-149, 2010.  T. W. Lee, 'Independent Component Analysis: Theory and Applications', Boston:Kluwer Academic Publishers, pp. 7-11, 1998.  A. Hyvarinen, 'Fast and robust fixed-point algorithms for independent component analysis' IEEE Transactions on Neural Networks, Vol. 10, pp 626-634, 1999  C. C. Chang and C. J. Lin LIBSVM – A Library Support Vector Machines. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm/||摘要:||
This study investigates the diagnosis of multiple faults that occur concurrently in the bearing through empirical mode decomposition and constrained independent component analysis. The vibration measurements are first decomposed into several intrinsic modal functions through the empirical mode decomposition method. The intrinsic mode functions that present obvious amplitude modulation phenomenon are selected to synthesize a new signal. The constrained independent component analysis is employed to extract the signal component which is highly correlated to the bearing fault features. The fast Fourier transform is utilized to obtain the frequency-domain features of the faulted signal, and the extracted features are compared with the one derived from the theoretical characteristics. The time-domain and frequency-domain characteristics of this independent component are quantified for the intelligent diagnosis through the support vector machine classifier.
|Appears in Collections:||機械工程學系所|
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