Abstract
Defects in structures will affect its natural vibrations. With the advent of pure data-driven modeling techniques such as Dynamic Mode Decomposition (DMD), the defected modes can be separated from the normal modes by using vibration data from various points on the structural element. In this work we simulate the vibrations of a cantilever beam in Abaqus® without defect and with different defects. We apply DMD to compute the spatial modes of vibration in each of these cases. Furthermore we train a Support Vector Machine (SVM) classifier with the Eigen-modes we have computed, to identify defects. We also analyze this data visually using t-SNE plots.
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Nagarajan, K., Ananthu, J., Menon, V.K., Soman, K.P., Gopalakrishnan, E.A., Ramesh, A. (2020). An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning. In: Reddy, A., Marla, D., Simic, M., Favorskaya, M., Satapathy, S. (eds) Intelligent Manufacturing and Energy Sustainability. Smart Innovation, Systems and Technologies, vol 169. Springer, Singapore. https://doi.org/10.1007/978-981-15-1616-0_71
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DOI: https://doi.org/10.1007/978-981-15-1616-0_71
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