EURASIP Journal on Applied Signal Processing 
Volume 2003 (2003), Issue 3, Pages 276-286
doi:10.1155/S1110865703210027

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

Vahid Emamian,1 Mostafa Kaveh,1 Ahmed H. Tewfik,1 Zhiqiang Shi,2 Laurence J. Jacobs,3 and Jacek Jarzynski2

 1Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis 55455, MN, USA
2Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332-0405, GA, USA
3School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta 30332-0355, GA, USA
Received 5 July 2001; Revised 15 August 2002

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.