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Synopsis

Stefan Bosse, Dirk Lehmhus, Robust detection of hidden material damages using low-cost external sensors and Machine Learning, 6th International Electronic Conference on Sensors and Applications. 15-30 Nov. 2019, MDPI

! Abstract: Machine Learning (ML) techniques are widely used in Structural Health Monitoring (SHM) and Non-destructive Testing (NDT), but the learning process, the learned models. and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damages in materials monitored using low-cost strain sensors. The investigation is performed using a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.

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