A physics-embedded deep-learning framework for efficient multi-fidelity modeling applied to guided wave based structural health monitoring

• Published in: Ultrasonics Vol. 141; p. 107325
• Main Authors: Nerlikar, Vivek, Miorelli, Roberto, Recoquillay, Arnaud, d’Almeida, Oscar
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-07-2024
• Subjects: Deep generative model; Deep learning multi-fidelity modeling; Digital Twin; Physics embedded machine learning; Simulation-to-experiment; Structural health monitoring; Ultrasonic guided waves; Physics embedded machine learning; Digital Twin; Deep learning multi-fidelity modeling; Deep generative model; Structural health monitoring; Ultrasonic guided waves; Simulation-to-experiment
• ISSN: 0041-624X; 1874-9968
• DOI: 10.1016/j.ultras.2024.107325

Health monitoring of structures using ultrasonic guided waves is an evolving technology with potential applications in monitoring pipelines, civil bridges, and aircraft components. However, the sensitivity of guided waves to external parameters affects the reliability of monitoring systems based on them. These influencing factors and experimental related factors cannot be perfectly modeled, which give rise to the discrepancy between numerical simulations and experimental measurements. Therefore, it is important to address this inevitable discrepancy and generate close-to-experiment simulations. In this work, we present a deep learning-based Digital Twin framework containing multi-fidelity modeling to reduce the discrepancy between measurements and simulations and a deep generative model to generate close-to-experiment guided wave responses by harnessing the vital characteristics of the two sources. These realistic simulations (close to experiment) can then be used in assessing the reliability of health monitoring system by generating probability of detection curves. Furthermore, they can also be used for augmenting the training data for a machine learning algorithm. We use a measurement dataset corresponding to crack propagation and simulations to validate the proposed framework. The results show that the discrepancy is indeed reduced to a great extent, furthermore, we also show that this framework enables the computation of probability of detection from close-to-experiment data as a direct consequence of rapid generation of realistic simulations.
•Structural health monitoring based on ultrasonic guided waves.•Discrepancy reduction between simulations and experiments.•A novel unifed deep neural network model blending simulations and experiments.•Enhanced ultrasonic guided waves signals generation.•Discrepancy quantification through phase and amplitude based misfit metrics.•Metamodel based computation of probability of detection.