19 April 2013 Singular value decomposition for novelty detection in ultrasonic pipe monitoring
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Abstract
Guided wave ultrasonics is an attractive technique for structural health monitoring, especially on pressurized pipes. However, civil infrastructure components, including pipes, are often subject to large environmental and operational variations that prevent traditional baseline subtraction-based approaches from detecting damage. We collect ultrasonic data on a large-scale pipe segment in its normal operating conditions and observe large environmental variations. We developed a damage detection method based on singular value decomposition (SVD) that is robust to those benign variations. We further develop an online novelty detection framework based on our SVD method to detect the presence of a mass scatterer on the pipe at the same time that we collect the data. We examine the framework with both synthetic simulations and field experimental data. The results show that the framework can effectively detect the presence of a scatterer and is robust to large environmental and operational variations.
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Chang Liu, Chang Liu, Joel B. Harley, Joel B. Harley, Yujie Ying, Yujie Ying, Irving J. Oppenheim, Irving J. Oppenheim, Mario Bergés, Mario Bergés, David W. Greve, David W. Greve, James H. Garrett, James H. Garrett, } "Singular value decomposition for novelty detection in ultrasonic pipe monitoring", Proc. SPIE 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, 86921R (19 April 2013); doi: 10.1117/12.2009891; https://doi.org/10.1117/12.2009891
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