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An Approach to Detect and Classify Defects in Cantilever Beams Using Dynamic Mode Decomposition and Machine Learning

  • Kailash NagarajanEmail author
  • J Ananthu
  • Vijay Krishna Menon
  • K. P. Soman
  • E. A. Gopalakrishnan
  • Ajith Ramesh
Conference paper
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Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 169)

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.

Keywords

Dynamic mode decomposition Structural health monitoring Support vector machines 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Kailash Nagarajan
    • 1
    Email author
  • J Ananthu
    • 2
  • Vijay Krishna Menon
    • 2
  • K. P. Soman
    • 2
  • E. A. Gopalakrishnan
    • 2
  • Ajith Ramesh
    • 3
  1. 1.Department of Mechanical EngineeringAmrita Vishwa VidyapeethamAmritapuriIndia
  2. 2.Center for Computational Engineering and Networking (CEN)Amrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia
  3. 3.Department of Mechanical EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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