Xin YANG

Damage imaging plays a crucial role in structural health monitoring (SHM) systems for fast and efficient damage assessment. Delay-and-sum (DAS) beamforming is a widely used algorithm in non-destructive testing for damage imaging, but its effectiveness is often compromised by the use of sparse ultrasonic transducer arrays and the difficulty in detecting progressive delamination larger than the wavelength using guided wave-based methods under fatigue loading. Although X-ray imaging offers detailed assessments of progressive delamination, its application is still limited due to the need to interrupt fatigue loading cycles and its high operational cost. To this end, we propose a novel Damage Imaging framework that uses the fine-tuned Conditional Diffusion Model for SHM systems (DI-CDM). Leveraging the powerful image generation capabilities of diffusion models, the framework was fine-tuned by combining DAS beamforming images derived from ultrasonic sparse array data with X-ray images captured during fatigue loading cycles of the composite structures. The proposed approach can generate damage images that reveal the progression of delamination size in the fatigue loading process. The framework was validated through numerical simulations and experimental data from NASA datasets for composite structures, demonstrating its potential and effectiveness by applying diffusion models in SHM applications to enable fast, high-resolution damage imaging.

Structural health monitoring is designed to detect and analyze damage in mechanical, aerospace, and industrial structures to prevent potential catastrophic failures. The effectiveness of identifying structural faults and operational damage significantly relies on the sensing technology. Multisensory data fusion, as one of the promising techniques, combines data from multiple sensor modalities for reliable and accurate fault diagnosis. In this paper, we propose a novel decision-level multisensory data fusion approach. This approach combines data from an ultrasonic guided-wave sensor modality and a vibration sensor modality for the first time, aiming to achieve precise and stable damage identification. The Bayesian probabilistic distributions from each sensor modality are obtained using a multi-level Metropolis–Hastings algorithm. Subsequently, a multilayer perceptron network is trained to automatically allocate confidence scores for each sensor modality. The final fusion results are derived from the proposed decision-level fusion approach, combining the Bayesian probabilistic distributions from each sensor modality with allocated confidence scores. Simulation and experiment results indicate that the decision-level fusion approach produces more accurate and reliable outcomes for damage identification compared to relying on a single modality, especially in the case of progressive damage.

This study presents a novel approach for real-time vision-based structural health monitoring, focusing on evaluating the deformation state of lattice structures. The structures are renowned for their remarkable recovery capabilities and exhibit similar mechanical responses under compressive loads. Despite these characteristics, quickly assessing the health status of the applied structure using knowledge from another related lattice structure is usually time-consuming and impractical. To address this, we propose to combine Gaussian process classification with transfer learning, termed TL-GPC, to detect damage states under compressive loads while also achieving uncertainty quantification. By employing structural deformations captured via the optical flow algorithm as inputs, the internal transfer kernel factor in TL-GPC is tailored to model knowledge transfer between source and target domain inputs. Experimental results show that the proposed TL-GPC model can deliver higher damage detection accuracy while ensuring stable uncertainty quantification in scenarios with limited and unbalanced experimental data.