Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections
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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: [email protected]
Mostafa Kaveh Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455, USA Email: [email protected]
Ahmed H. Tewfik Department of Electrical & Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455, USA Email: [email protected]
Zhiqiang Shi Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA Email: [email protected]
Laurence J. Jacobs School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA Email: [email protected]
Jacek Jarzynski Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA Email: [email protected] 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 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 and phrases: acoustic signals, classification, mechanical failure, neural networks, subspace projections, SOM, PCA, RBF.
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INTRODUCTION
An acoustic emission (AE) signal is an ultrasonic wave emitted from the deformation of materials. Specifically, AE is the transient wave resulting from the sudden release of stored
energy during a deformation and failure process, such as fretting or crack growth in a material. The AE signal conveys useful information about the fatigue behavior of a specimen, and is one of the several nondestructive inspection methods for automatic fault monitoring in mechanical systems.
Robust Clustering of Acoustic Em
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