Transformation of Guided Ultrasonic Wave Signals from Air Coupled to Surface Bounded Measurement Systems with Machine Learning Algorithms for Training Data Augmentation.

Guided ultrasonic waves (GUW) analysis is a well-investigated method for structural health monitoring (SHM) applications. For plate-like structures, the pitch-catch technique is a popular choice since it offers the possibility to investigate a large area with a small number of sensors. This method requires a large amount of data to be analyzed to detect and localize damage. That, with the consequence that besides the presence of damage, also environmental influences like temperature and load will change the GUW signals. In addition, the location, size, and type of the damage will result in different changes of the GUW signals. Data-driven methods require sufficient data, requiring data augmentation. In order to get closer to this goal, this study aims to demonstrate the conversion of GUW signals measured with an air-coupled measurement system (ACMS) into signals measured with Piezoelectric Wafer Active Sensors (PWAS). This would allow the fast measurement of GUW data with ACMS at different positions of a plate-like specimen and translate it to a surface-bonded PWAS signal without the time-consuming process of transducer mounting. In this study, it is assumed that the measurement methods are not independent from each other when they are measured at the same position. To obtain the transfer function from ACMS to PWAS, GUW signals were measured both with ACMS and PWAS for different positions of artificial damage. Since both signal classes are physically dependent, it should be possible to determine the transfer function with machine learning (ML) methods. As input, the ACMS time-dependent signal or signal features are used, while the PWAS signals serve as labels for the training process. We are evaluating different ML-based transfer model architectures with respect to their suitability for signal or signal feature transformation, e.g., ANN, CNN, and LSTM-based networks, with a particular focus on Autoencoders.