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

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.

Guided wave imaging of composite plates using passive acquisitions by fiber Bragg gratings.

In this paper, imaging results of defects in composite plates using guided wave-based algorithms, such as delay and sum and Excitelet, are presented. Those algorithms are applied to passive data for which the signal corresponding to each emitter-receiver couple is recovered as a result of the cross correlation of the ambient noise measured simultaneously by the two sensors. The transition to passive imaging allows the use of lighter sensors that are unable to emit ultrasonic waves, such as fiber Bragg gratings (FBGs) sensors on optical fibers, which are used in this study. The imaging results presented here show the feasibility of active and passive imaging in composite plates using FBGs as receivers, reducing the impact of the acquisition system on the structure in the context of structural health monitoring.

Passive guided wave tomography for structural health monitoring.

In this paper the authors present a baseline-free quantitative method for imaging corrosion flaws in thin plates. It only requires an embedded guided wave sensor network used in a fully passive way, i.e., without active emission of waves. This method is called passive guided wave tomography. The aim of this development is the use of this method for the structural health monitoring of critical structures with heavy limitations on both sensor's intrusiveness and diagnostic's reliability because it allows the use of sensors that cannot emit elastic waves such as fiber Bragg gratings, which are less intrusive than piezoelectric transducers. The idea consists in using passive methods in order to retrieve the impulse response from elastic diffuse fields-naturally present in structures-measured simultaneously between the sensors. In this paper, two passive methods are studied: the ambient noise cross-correlation and the passive inverse filter. Once all the impulse responses between the sensors are retrieved, they are used as input data to perform guided wave tomography.