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Authors: Ishan Khurjekar (University of Florida) , Joel Harley (University of Florida)
Damage detection and localization remain challenging research areas in structural health monitoring. Guided wave-based methods that utilize signal processing tools (e.g., matched field processing and delay-and-sum localization) have enjoyed success in damage detection. To locate damage, such techniques rely on a model of wave propagation through materials. Measured data is then compared with these models to determine the origin of a wave. As a result, the analytical model and actual data may have a mismatch due to environmental variations or a lack of knowledge about the material. Deep neural networks are a class of machine learning algorithms that learn a non-linear functional mapping. The paper presents a deep neural network-based approach to damage localization. We use simulated data to assess the performance of localization frameworks under varying levels of noise and other uncertainty in our ultrasonic signals.
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How to Cite: Khurjekar, I. & Harley, J. (2019) “Deep neural network-based guided wave damage localization”, Review of Progress in Quantitative Nondestructive Evaluation.(0).