Open Access

Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

  • Vahid Emamian1Email author,
  • Mostafa Kaveh1,
  • Ahmed H. Tewfik1,
  • Zhiqiang Shi2,
  • Laurence J. Jacobs3 and
  • Jacek Jarzynski2
EURASIP Journal on Advances in Signal Processing20032003:248703

DOI: 10.1155/S1110865703210027

Received: 5 July 2001

Published: 13 March 2003

Abstract

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE) events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM) neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT) is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

Keywords

acoustic signals classification mechanical failure neural networks subspace projections SOM PCA RBF

Authors’ Affiliations

(1)
Department of Electrical & Computer Engineering, University of Minnesota
(2)
Woodruff School of Mechanical Engineering, Georgia Institute of Technology
(3)
School of Civil & Environmental Engineering, Georgia Institute of Technology

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003