NOI-POMDP: A Noise-Explicit POMDP Framework for Structural Health Monitoring

Abstract

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.