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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Two ICA Algorithms Applied to BSS in Non-destructive Vibratory Tests
verfasst von : Juan-José González de-la-Rosa, Carlos G. Puntonet, R. Piotrkowski, I. Lloret, Juan-Manuel Górriz
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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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.