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EURASIP Journal on Applied Signal Processing 2003:3, 276–286 c 2003 Hindawi Publishing

EURASIP Journal on Applied Signal Processing 2003:3, 276–286 c 2003 Hindawi Publishing Corporation Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections Vahid Emamian Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455, USA Email: emamian@ieee.org Mostafa Kaveh Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455, USA Email: mos@ece.umn.edu Ahmed H. Tewfik Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455, USA Email: tewfik@ece.umn.edu Zhiqiang Shi Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA Email: ShiZ@corning.com Laurence J. Jacobs School. | EURASIP Journal on Applied Signal Processing 2003 3 276-286 2003 Hindawi Publishing Corporation Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections Vahid Emamian Department of Electrical Computer Engineering University of Minnesota 200 Union St. SE Minneapolis MN 55455 USA Email emamian@ieee.org Mostafa Kaveh Department of Electrical Computer Engineering University of Minnesota 200 Union St. SE Minneapolis MN 55455 USA Email mos@ece.umn.edu Ahmed H. Tewfik Department of Electrical Computer Engineering University of Minnesota 200 Union St. SE Minneapolis MN 55455 USA Email tewfik@ece.umn.edu Zhiqiang Shi Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332-0405 USA Email ShiZ@corning.com Laurence J. Jacobs School of Civil Environmental Engineering Georgia Institute of Technology Atlanta GA 30332-0355 USA Email ljacobs@ce.gatech.edu Jacek Jarzynski Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332-0405 USA Email jacek.jarzynski@me.gatech.edu Received 5 July 2001 and in revised form 15 August 2002 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 interferences. 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 .