Data-driven Parameterizable Generative Adversarial Networks for Synthetic Data Augmentation of Guided Ultrasonic Wave Sensor Signals

Abstract

Detecting and characterizing hidden damages in composite materials like Fibre-Metal Laminates (FML) remains a challenge. Guided Ultrasonic Waves (GUW) or X-ray imaging are commonly used to detect these damages, but their interpretation remains limited. Data-driven predictor models can detect damages in structures using GUW time-dependent signals, but the training data lacks variance, quality, and sufficient coverage of the hyper-parameter space. Synthetic data augmentation is often the only solution to create robust and generalized damage predictor models. Synthetic sensor data can be generated using model-based, model-assisted, or model-free methods. However, computed GUW signals show poor reality conformance due to too much constraints and simplifications. Recent developments in Generative Adversarial (Neural) Networks (GAN), commonly driven by a random generation process [1], include deterministic style vectors to generate specific signal data, determining constraints such as damage size, position, transducer positions, material and environmental properties. These new architectures aim to reduce the impact of environmental changes on data-driven damage predictor models by using parameterizable synthetic data generation. We will elaborate these new architectures and discuss the possibilities and challenges of using such GAN models for data generation of US signals in GUW-based SHM applications, as originally proposed in [2]. We will have a focus on low-quality real measuring data used for training and composite materials (e.g., FML) with hidden damages, and show some preliminary results. [1] Karras et al. "A style-based generator architecture for generative adversarial networks." Proceedings of the IEEE/CVF 2019 [2] Virupakshappa et al. "Using Generative Adversarial Networks to Generate Ultrasonic Signals." 2020 IEEE International Ultrasonics Symposium (IUS). IEEE, 2020