The Research on Blind Source Separation Method for Vibration Signals Base on the Second-Order Statistics

Abstract:

A blind source separation (BSS) method base on second-order statistics was applied to separate the vibration mixtures based on the temporal structure of vibration signals. The single delay covariance matrix was used to compute the whiten matrix. A set of covariance matrices with different time delay was joint approximatively diagonalized to obtain their ‘average eigenstructure’. The separation of the real vibration mixtures from two motors illustrate that it is feasible to applied this method to the research of the health monitoring of the gearbox.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 1290-1293
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.1290
• Online since: February 2011
• Authors: Hong Xian Ye, Xiao Feng Zhou
• Keywords: Blind Source Separation (BSS), Fault Diagnosis, Second-Order Statistic, Vibration Signal

References

[1] Gelle G, Colas M, Christine S, in: Blind source separation: A new pre-processing tool for rotating machines monitoring[J]? IEEE Transactions on Instrumentation and Measurement, 2003, 52(3): 790-795.

DOI: 10.1109/tim.2003.814356

[2] Yang S X, Jiao W D, Wu Z T, in: Independent Component Analysis Based Networks for Fault Features Extraction and Classification of Rotating Machines [J]. Chinese Journal of Mechanical Engineering, 2004, 3: 45-49.

DOI: 10.3901/jme.2004.03.045

[3] Cardoso J F, Laheld B H, in: Equivariant Adaptive Source Separation. IEEE Trans on Signal Processing [J], 1996, 44(12): 3017-3030.

DOI: 10.1109/78.553476

[4] Hyvärinen A, Karhunen J, Oja E, in: Independent Component Analysis[M]. 2001, John Wiley & Sons. INC.

[5] Hyvarinen A, in: A family of fixed-point algorithms for independent component analysis[C]. Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP-97, 1997. 5: 3917-3920.

DOI: 10.1109/icassp.1997.604766

[6] Ypma A, in: Learning methods for machine vibration analysis and health monitoring[D]. 1998. Netherlands, Delft University of Technology.

[7] Belouchrani A, Abed-Meraim K, Cardoso J F, etc, in: A blind source separation technique using second order statistics[J]. IEEE Trans. on Signal Processing, 1997; 45(2): 434-444.

DOI: 10.1109/78.554307

[8] Belouchrani A, Cichocki S, in: Blind separation of second-order nonstationary and temporally colored sources[C]. 2001. Singapore.

DOI: 10.1109/ssp.2001.955318

[9] Andreas Ziehe, Pavel Laskov, Guido Nolte, etc, in: A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation[J]. Machine Learning Research, 2004; 5(Jul): 777-800.

[10] Pham, Dinh Tuan, in: Joint Approximate Diagonalization Of Positive Definite Hermitian Matrices[J]. Society for Industrial and Applied Mathematics, 2001; 22(4): 1136-1152.

DOI: 10.1137/s089547980035689x

[11] Herring K T, Mueller A V and Staelin D H, in: Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(10): 3406-3415.

DOI: 10.1109/tgrs.2009.2022325

[12] Ye H X, Yang S X, Yang J X, in: A Rapid Second-Order Blind Source Separation Algorithm for Vibration Signals[J]. Journal of Vibration and Shock, 2008, 27(7): 79-82.