Structural Health Monitoring (SHM) aims to enhance infrastructure reliability by reducing unnecessary inspections, minimizing downtime, and preventing failures. However, noise in sensor data remains a persistent challenge, often arising from sensor degradation, environmental variability, or transmission errors. This noise is typically non-stationary and can follow time-varying patterns that affect the reliability of observations. Partially Observable Markov Decision Processes (POMDPs) provide a framework for optimizing inspection and maintenance policies to achieve cost-effective decision-making without compromising safety. Yet, POMDP formulations often rely on bounding assumptions such as access to high-quality, noise-free observations, and standard models typically do not update this assumption during operation. This simplification overlooks the complex nature of noise in monitoring data, causing belief updates to become overconfident and leading to suboptimal, locally optimal maintenance policies. To address this, the study proposes a Noise-aware Observation-Integrated POMDP (NOI-POMDP) framework that explicitly incorporates the noise-generating process as a dynamic and learnable component of the observation model (Fig. 1). The SHM agent updates beliefs from noisy observations while the underlying NOI-POMDP explicitly represents how time-varying sensor noise affects state information, enabling adaptive belief updates and improving long-run decision quality. The methodology is demonstrated through a beam SHM case study where structural condition is identified using vibration-based features derived from sensor measurements. Across simulated deterioration processes and varying noise patterns, the NOI-POMDP framework demonstrates improved decision quality compared to conventional fixed-noise POMDP formulations. Results show increased policy robustness under severe noise variability and improved state estimation accuracy, leading to balanced inspections and lower long-term lifecycle costs while maintaining safety constraints.
Junyi Li, E. Tronci· e-Journal of Nondestructive...· 0 citations
Recent advances in Physics-Informed Neural Networks (PINNs) have opened new possibilities for integrating structural dynamics and data-driven learning in Structural Health Monitoring (SHM). This work presents a physics-informed framework for input load estimation and virtual sensing of offshore wind turbine support structures, where the governing dynamics of the system are embedded directly into the learning process. Unlike purely data-driven models that require extensive labeled datasets, the proposed approach leverages known physical relationships among displacement, velocity, acceleration, and external loads to enhance interpretability and generalization. The method adopts an encoder–decoder neural architecture that maps measured accelerations and strains to a reduced-order modal space before decoding the corresponding dynamic responses and reconstructing the applied loads through embedded structural dynamics relationships. Physical consistency is enforced through the equations of motion and differential constraints between displacement, velocity, and acceleration, while automatic differentiation ensures temporal consistency without requiring explicit load data during training. This hybrid approach captures the temporal and spatial evolution of loads even with limited or noisy measurements. The framework is first validated on numerical simulations of an offshore wind turbine, accurately recovering unmeasured input loads and structural responses across diverse operating conditions. It is then demonstrated using experimental vibration data, confirming its robustness to sensor noise and sparse instrumentation. Results show that the proposed physics-informed strategy can recover complex loading patterns and provide virtual measurements that are otherwise inaccessible in practice. Overall, study advances the use of PINNs for inverse input load estimation problem in SHM, offering a computationally efficient and generalizable tool for condition monitoring and fatigue assessment of large-scale energy infrastructure.
Azin Mehrjoo, E. Tronci, Babak Moaveni· e-Journal of Nondestructive...· 0 citations