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Two ICA Algorithms Applied to BSS in Non-destructive Vibratory Tests

  • Juan-José González de-la-Rosa
  • Carlos G. Puntonet
  • R. Piotrkowski
  • I. Lloret
  • Juan-Manuel Górriz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

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.

Keywords

Acoustic Emission Independent Component Analysis Independent Component Acoustic Emission Signal Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juan-José González de-la-Rosa
    • 1
  • Carlos G. Puntonet
    • 2
  • R. Piotrkowski
    • 1
  • I. Lloret
    • 1
  • Juan-Manuel Górriz
    • 2
  1. 1.Research Group TIC168 – Computational Instrumentation and Industrial Electronics, EPSAUniversity of CádizAlgeciras-CádizSpain
  2. 2.Department of Architecture and Computers TechnologyUniversity of GranadaGranadaSpain

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