Abstract
As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semianechoic chamber demonstrate the effectiveness of the presented methods.
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References
Lyon RH (1987) Machinery noise and diagnostics. Butterworths, Boston
Lin J (2001) Feature extraction of machine sound using wavelet and its application in fault diagnosis. NDT&E Int 34:25–30
Gelle G, Colas M, Delaunay G (2000) Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis. Mech Syst Signal Process 14(2):427–442
Shibata K, Takahashi A, Shirai T (2000) Fault diagnosis of rotating machinery through visualisation of sound signals. Mech Syst Signal Process 14(1):229–241
Cao XR, Ruey-wen L (1996) General approach to blind source separation. IEEE Trans Signal Process 44(2):562–571
Comon P (1994) Independent component analysis, a new concept? Signal Process 36(1):287–314
Cardoso JF, Soulomiac AS (1996) Jacobi angles for simultaneous diagonalization. SIAM J Matrix Anal Appl 17(1):161–164
Belouchrani A, Abed-Meraim K, Cardoso JF, et al. (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45(1):434–444
Yeredor A (2000) Blind source separation via the second characteristic function. Signal Process 80(2):897–902
Moreau E (2001) A generalization of joint-diagonalization criteria for source separation. IEEE Trans Signal Process 49(2):530–541
Tugnait JK (1999) Adaptive blind separation of convolutive mixtures of independent linear signals. Signal Process 73:139–152
Gaeta M, Briolle F, Esparcieux P (1997) Blind separation of sources applied to convolutive mixtures in shallow water. In: Proceedings of SPW-HOS’97, pp 340–343
Lathauwer LD, Callaerts D, Moor BD, et al. (1995) Fetal electrocardiogram extraction by sources subspace separation. In: proceedings of SPW-HOS’95, pp 134–138
Van der Veen AJ (1998) Algebraic methods for deterministic blind beamforming. In: Proceedings of the IEEE 86(9):1987–2008
Press SJ (1982) Applied multivariate analysis, 2nd edn. Krieger, New York
Kompella MS, Davies P, Bemhard RJ, et al. (1994) A technique to determine the number of incoherent sources contributions to the response of a system. Mech Syst Signal Process 8(3):363–380
Nashed MZ (1981) Operator-theoretical and computational approaches to ill-conditioned problem with application to antenna theory. IEEE Trans Antenna Propagation 29(1):220–231
Bendat JS, Piersol AG (1986) Random data, analysis and measurement procedures. Wiley, New York
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Zhong, Z.M., Chen, J., Zhong, P. et al. Application of the blind source separation method to feature extraction of machine sound signals. Int J Adv Manuf Technol 28, 855–862 (2006). https://doi.org/10.1007/s00170-004-2353-7
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DOI: https://doi.org/10.1007/s00170-004-2353-7