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Smart Structures and Systems Volume 2, Number 3, July 2006 , pages 253-274 DOI: https://doi.org/10.12989/sss.2006.2.3.253 |
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Wavelet-based feature extraction for automatic defect classification in strands by ultrasonic structural monitoring |
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Piervincenzo Rizzo and Francesco Lanza di Scalea
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| Abstract | ||
| The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multi-dimensional analysis can provide excellent classification performance for notch-type defects in strands. | ||
| Key Words | ||
| multi-wire strands; guided ultrasonic waves; damage index; feature extraction; discrete wavelet transform; artificial neural networks. | ||
| Address | ||
| NDE & Structural Health Monitoring Laboratory, Department of Structural Engineering, University of California, San Diego, 9500 Gilman Drive, M.C. 0085, La Jolla, CA 92093-0085, USA | ||