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Publication Type : Conference Paper
Publisher : Springer Singapore
Source : Smart Innovation, Systems and Technologies, Springer Singapore, Singapore (2020)
ISBN : 9789811516160
Campus : Coimbatore, Kochi
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering, Center for Computational Engineering and Networking (CEN), Electronics and Communication
Year : 2020
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.
Cite this Research Publication : K. Nagarajan, Ananthu, J., Menon, V. Krishna, Dr. Soman K. P., Gopalakrishnan, E. A., and Dr. Ajith Ramesh, “An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning”, in Smart Innovation, Systems and Technologies, Singapore, 2020.