Two ICA Algorithms Applied to BSS in Non-destructive Vibratory Tests
Two independent component analysis (ICA) algorithms have been applied for blind source separation (BSS) in a synthetic, multi-sensor scenario, within a non-destructive pipeline test. The first one, CumICA, is based in the computation of the cross-cumulants of the mixed observed signals, and needs the aid of a digital high-pass filter to achieve the same SNR (up to -40 dB) as the second algorithm, Fast-ICA. Vibratory signals were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Different types of commonly observed source signals are linearly mixed, involving acoustic emission (AE) sequences, impulses and other parasitic signals modelling human activity. Both ICA algorithms achieve to separate the impulse-like and the AE events, which often are associated to cracks or sudden non-stationary vibrations.
KeywordsAcoustic Emission Independent Component Analysis Independent Component Acoustic Emission Signal Independent Component Analysis
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- 1.de la Rosa, J.J.G., Lloret, I., Ruzzante, J., Piotrkowski, R., Armeite, M., Pumarega, M.L.: Higher-order characterization of acoustic emission signals. In: CIMSA 2005, Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applicacions, Giardini Naxos, Italy, July 20-22 (2005); ISBN 0-7803-9026-1; IEEE Catalog Number 05EX1037 (2005), pp. 296–300 Paper CM5027. Oral Presentation in the Session 16 Advanced Signal Processing 2Google Scholar
- 2.de la Rosa, J.J.G., Puntonet, C.G., Lloret, I.: An application of the independent component analysis to monitor acoustic emission signals generated by termite activity in wood. Measurement (Ed. Elsevier) 37, 63–76 (2005); Available online (October 12, 2004)Google Scholar
- 3.Piotrkowski, R., Gallego, A., Castro, E., García-Hernéandez, M., Ruzzante, J.: Ti and Cr nitride coating/steel adherence assessed by acoustic emission wavelet analysis. Non Destructive Testing and Evaluation (NDT and E) International (Ed. Elsevier) 8, 260–267 (2005)Google Scholar
- 6.Mansour, A., Barros, A.K., Onishi, N.: Comparison among three estimators for higher-order statistics. In: The Fifth International Conference on Neural Information Processing, Kitakyushu, Japan (1998)Google Scholar
- 7.Hyvärinen, A., Oja, E.: Independent Components Analysis: A Tutorial. Helsinki University of Technology, Laboratory of Computer and Information Science (1999)Google Scholar
- 9.Swami, A., Mendel, J.M., Nikias, C.L.: Higher-Order Spectral Analysis Toolbox User’s Guide (2001)Google Scholar