Search for: All records

Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing techniques are commonly employed. The detection performance is affected by a mismatch between the wave propagation model and experimental wave data. External variations, such as temperature, which are difficult to model, also affect the performance. While deep learning models can be an alternative detection method, there is often a lack of real-world training datasets. In this work, we counter this challenge by training an ensemble of variational autoencoders only on simulation data with a wave physics-guided adversarial component. We set up an experiment with non-uniform temperature variations to test the robustness of the methods. We compare our scheme with existing deep learning detection schemes and observe superior performance on experimental data. 

Guided ultrasonic wave localization systems use spatially distributed sensor arrays and wave propagation models to detect and locate damage across a structure. Environmental and operational conditions, such as temperature or stress variations, introduce uncertainty into guided wave data and reduce the effectiveness of these localization systems. These uncertainties cause the models used by each localization algorithm to fail to match with reality. This paper addresses this challenge with an ensemble deep neural network that is trained solely with simulated data. Relative to delay-and-sum and matched field processing strategies, this approach is demonstrated to be more robust to temperature variations in experimental data. As a result, this approach demonstrates superior accuracy with small numbers of sensors and greater resilience to spatially nonhomogeneous temperature variations over time.

 

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.