A BLIND SOURCE SEPARATION METHOD FOR CONVOLVED MIXTURES BY NON-STATIONARY VIBRATION SIGNALS

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

8
Reader(s)
16
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 6 , ISSUE 3 (June 2013) > List of articles

A BLIND SOURCE SEPARATION METHOD FOR CONVOLVED MIXTURES BY NON-STATIONARY VIBRATION SIGNALS

Ye Hongxian * / Li Wenchang * / Hu Xiaoping *

Keywords : Mechanical Vibration, Blind Source Separation, Convolutive Mixture, Non-stationary.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 3, Pages 973-992, DOI: https://doi.org/10.21307/ijssis-2017-575

License : (CC BY-NC-ND 4.0)

Received Date : 02-November-2012 / Accepted: 03-May-2013 / Published Online: 05-June-2013

ARTICLE

ABSTRACT

A simple and feasible of BSS method to separate the convolution mixture of non-stationary vibration signals is present in this paper. The method is carried out by means of two iterative procedures: The first procedure is to estimate the coefficients of the filters. The independence criterion is used and the unknown filters are obtained by a back propagation procedure by means of simplifying the coefficients of filters. The next procedure is to estimate the source vibration signals. The coupled vibration signals are obtained by means of the filters gotten from the former procedure and the estimation sources are obtained through decoupled procedure. Simulation and experiment results show that the method is effective. This improved method can be used to separate convolutive mixtures with non-stationary mechanical vibrations.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] A. Ypma, “Learning Methods for Machine Vibration Analysis and Health Monitoring”, Doctor of Engineering thesis, Pattern Recognition Group, Dept. of Applied Physics, Delft University of Technology, Netherlands, 1998.
[2] Z. N. Li, W. B. Liu and X. B. YI, “Underdetermined Blind Source Separation Method of Machine Faults Based on Local Mean Decomposition”, Chinese Journal of Mechanical Engineering, Vol. 47, No.7, pp. 97-102, Apr. 2011.
[3] W. F. Wu, X. H. Chen and X. J. SU, “Blind Source Separation of Single-channel Mechanical Signal Based on Empirical Mode Decomposition”, Chinese Journal of Mechanical Engineering, Vol. 47, No.4, pp. 12-16, Apr. 2011.
[4] G. Colas, “Blind Source Separation: a Tool for Rotating Machine Monitoring by Vibration Analysis” International Journal of Sound and Vibration, Vol. 248, No.5, pp. 865-885, Dec. 2001.
[5] G. Guillaume and S. Christine, “Blind Source Separation: A New Pre-Processing Tool for Rotating Machines Monitoring”? IEEE Transactions on Instrumentation and Measurement, Vol. 52, No.3, pp. 790-795, June. 2003.
[6] H. X. Ye, S. X. Yang and J. X. Yang, “Temporal Blind Source Separation Algorithm for Convolution Mixtures with Multi Vibration Sources”, Chinese Journal of Mechanical Engineering, Vol. 45, No. 1, pp.189-194,199, Jan. 2009.
[7] M. Parvaix and L.Girin, “Informed Source Separation of Linear Instantaneous Under-Determined Audio Mixtures by Source Index Embedding”, IEEE Transactions on Audio, Speech and Language Processing, Vol. 19, No. 6, pp. 1721-1733, Aug. 2011.
[8] P. Tichavsky and Z. Koldovsky, “Weight Adjusted Tensor Method for Blind Separation of Underdetermined Mixtures of Nonstationary Sources”, IEEE Transactions on Signal Processing, Vol. 59, No. 3, pp. 1037-1047, Mar. 2011.
[9] D. Farina, M. F. Lucas and C. Doncarli, “Optimized Wavelets for Blind Separation of Nonstationary Surface Myoelectric Signals”, IEEE Transactions on Biomedical Engineering, Vol. 55, No. 1, pp. 78-86, Jan. 2008.
[10] R. Guidara, S. Hosseini and Y. Deville, “Blind Separation of Nonstationary Markovian Sources Using an Equivariant Newton–Raphson Algorithm”, IEEE Signal Processing Letters, Vol. 16, No. 5, pp. 426-429, May. 2009.
[11] H. Rimminen, J. Lindstrom and R. Sepponen, “Positioning Accuracy and Multi-Target Separation With a Human Tracking System Using Near Field Imaging”, International Journal on Smart Sensing and Intelligent Systems, Vol. 2, No.1, pp. 156–175, Mar. 2009.
[12] B. Makkiabadi, D. Jarchi and S. Sanei, “A New Time Domain Convolutive BSS of Heart and Lung Sounds, Acoustics”, IEEE International Conference on Speech and Signal Processing (ICASSP), Vol. 25-30, pp. 605 – 608, Mar. 2012, Kyoto, Japan.
[13] G. P. Cedric, C. Marco and C. Brunner, C. Jutten and P. Gert, “Nonstationary Brain Source Separation for Multiclass Motor Imagery”, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp.469-478, Feb. 2010.
[14] S. Choi and A. Cichocki, “Blind Separation of Nonstationary Sources in Noisy Mixtures”, International Journal of Electronics Letters, Vol. 36, No.9, pp. 848-849, Apr. 2000.
[15] T. Ishibashi, K. Inoue and H. Gotanda, “Studies on Estimation of the Sources Number in Blind Source Separation Problems”, International Joint Conference SICE-ICASE, pp. 5169-5174, Oct. 2006, Busan, Korea.
[16] N. Pan, X. Wu and Y. L. Chi, X. Q. Liu and C. Liu, “Machine Fault Diagnosis Based on Frequency-Domain Blind Deconvolution”. Proceedings of 2011 International Conference on Modeling, Identification and Control, pp. 63-68, June. 2011, Shanghai, China.
[17] Y. Y. Na and J. Yu, “Kernel and Spectral Methods for Solving the Permutation Problem in Frequency Domain BSS”, WCCI 2012 IEEE World Congress on Computational Intelligence, pp.1-8, June. 2012, Brisbane, Australia.
[18] S. M. Naqvi, Y. Zhang, T. Tsalaile, S. Saneiz and J. A. Chambers, “Evaluation of Emerging Frequency Domain Convolutive Blind Source Separation Algorithms Based on Real Room Recordings”, 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 345 - 348, July. 2008, Darmstadt, Germany.
[19] T. T. Liu, X. M. Ren, “A Blind De-convolution Technique for Machine Fault Diagnosis”, 2009 Second International Conference on Information and Computing Science”, pp. 232-235, May. 2009, Manchester, England, UK.
[20] I. Russell, J. T. Xi and A. Mertins, “Blind Source Separation of Nonstationary Convolutively Mixed Signals in the Subband Domain”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 5, pp. 481-484, May. 2004, Quebec, Canada.
[21] S. B. Han, J. Cui and P. Li, “Post-processing for Frequency-domain Blind Source Separation in Hearing Aids”, 7th International Conference on Information, Communications and Signal Processing, pp. 1 – 5, 8-10, Dec. 2009, Beijing, China.
[22] T. Nguyen and C. Jutten, “Blind Source Separation for Convolutive Mixtures”, IEEE Transactions on Signal Processing, Vol. 45, No. 2, pp. 209-229, July. 1995.
[23] Advantech CO. LTD, PCL-1800 Manual, 2005.

EXTRA FILES

COMMENTS