Kumar, A.; Guha, A.; Banerjee, S. Transforming Simulated Data into Experimental Data Using Deep Learning for Vibration-Based Structural Health Monitoring. Mach. Learn. Knowl. Extr.2024, 6, 18-40.
Kumar, A.; Guha, A.; Banerjee, S. Transforming Simulated Data into Experimental Data Using Deep Learning for Vibration-Based Structural Health Monitoring. Mach. Learn. Knowl. Extr. 2024, 6, 18-40.
Kumar, A.; Guha, A.; Banerjee, S. Transforming Simulated Data into Experimental Data Using Deep Learning for Vibration-Based Structural Health Monitoring. Mach. Learn. Knowl. Extr.2024, 6, 18-40.
Kumar, A.; Guha, A.; Banerjee, S. Transforming Simulated Data into Experimental Data Using Deep Learning for Vibration-Based Structural Health Monitoring. Mach. Learn. Knowl. Extr. 2024, 6, 18-40.
Abstract
While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.
Keywords
Domain Transfer; Structural Health Monitoring; Vibration-based; Deep Leaning; Experimental Data Generation
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
