Multi-Fault Diagnosis of Rotating Machinery Based on Spatially Constrained ICA

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Most conventional methods cannot get effective performance during multi-fault diagnosis of rotating machines. Blind source separate techniques have applied to this problem, but the results show some limitations. Some prior knowledge considering the spatial or temporal characteristics of signals can be incorporated into these BSS approaches. Here spatial topography information will be chosen as spatial constraints and combine them with FastICA algorithm to get spatially constrained ICA (SCICA) method. This new technique will deal with multi-fault signals of rotating machinery through simulation process. SCICA is effective to multi-fault diagnosis and can separate all the source signals.

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985-988

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October 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] L. Wei, J.C. Rajapakse: ICA with reference. Neurocomputing, 69(16), pp.2244-2257, (2006).

DOI: 10.1016/j.neucom.2005.06.021

Google Scholar

[2] Mi Jian-Xun: A novel algorithm for independent component analysis with reference and methods for its applications. PLOS ONE, 9(5), p. e93984, (2014).

DOI: 10.1371/journal.pone.0093984

Google Scholar

[3] Z.Y. Wang, J. Chen, G.M. Dong and Y. Zhou: Constrained independent component analysis and its application to machine fault diagnosis. Mechanical Systems and Signal Processing, 25(7), pp.2501-2512, (2011).

DOI: 10.1016/j.ymssp.2011.03.006

Google Scholar

[4] A. Hyvarinen, E. Oja: Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), pp.411-430, (2000).

DOI: 10.1016/s0893-6080(00)00026-5

Google Scholar

[5] C.W. Hesse, C.J. James: The FastICA algorithm with spatial constraints. IEEE Signal Process Letters, 12(11), pp.792-795, (2005).

DOI: 10.1109/lsp.2005.856867

Google Scholar

[6] C.W. Hesse, C.J. James: On semi-blind source separation using spatial constraints with applications in EEG analysis. IEEE Transactions on Biomedical Engineering, 53(12), pp.2525-2534, (2006).

DOI: 10.1109/tbme.2006.883796

Google Scholar

[7] M. De. Vos, L. De. Lathauwer and S. Van Huffel: Spatilly constrained ICA algorithm with an application in EEG processing. Signal Processing, 91(8), pp.1963-1972, (2011).

DOI: 10.1016/j.sigpro.2011.02.019

Google Scholar

[8] Mt. Akhtar, W. Mitsuhashi and C.J. James: Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal processing, 92(2), pp.401-416, (2012).

DOI: 10.1016/j.sigpro.2011.08.005

Google Scholar