Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals; a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.
References
[1]
Shao, H., Shi, X.H. and Li, L. (2011) Power Signal Separation in Milling Process Based on Wavelet Transform and Inde-pendent Component Analysis. International Journal of Machine Tools and Manufacture, 51, 701-710.
https://doi.org/10.1016/j.ijmachtools.2011.05.006
[2]
Davies, M.E. and James, C.J. (2007) Source Separation Using Single Channel ICA. Signal Processing, 87, 1819-1832. https://doi.org/10.1016/j.sigpro.2007.01.011
[3]
Huang, N.E., Shen, Z., Long, S.R., et al. (1998) The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, The Royal Society, 903-995.
https://doi.org/10.1098/rspa.1998.0193
[4]
Wu, Z.H. and Huang, N.E. (2009) Ensemble Empirical Mode Decomposi-tion: A Noise Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 1, 1-41.
https://doi.org/10.1142/S1793536909000047
[5]
Comon, P. (1994) Independent Component Analysis: A New Con-cept. Signal Pro- cessing, 36, 287-314. https://doi.org/10.1016/0165-1684(94)90029-9
[6]
Hyvärinen, A. (1999) Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks, 10, 626-634.
https://doi.org/10.1109/72.761722
[7]
Vicente, Z. and Pierre, C. (2010) Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size. IEEE Trans Neural Networks, 21, 248-261. https://doi.org/10.1109/TNN.2009.2035920
[8]
Mijovic, B., De Vos, M., Gligo Rjevic, I., et al. (2010) Combin-ing EMD with ICA for Extracting Independent Sources from Single Channel and Two-Channel Data. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5387-5390.
https://doi.org/10.1109/IEMBS.2010.5626482
[9]
Konar, P. and Chattopadhyay, P. (2011) Bearing Fault Detection of Induction Motor Using Wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11, 4203-4211. https://doi.org/10.1016/j.asoc.2011.03.014
