Health indicator modeling leveraging time-independent and time-dependent subtasks with adaptive standardization and physics-based Bayesian optimization for aeronautical structures

Abstract

Monitoring the structural integrity of aeronautical structures is critical for safety, reducing maintenance costs, and enabling predictive maintenance. However, raw structural health monitoring (SHM) data are often noisy, high-dimensional, and difficult to interpret. To enable condition-based maintenance, it is essential to extract health indicators (HIs)—quantitative representations of structural degradation that evolve consistently over time. Accurately extracting HIs for composite structures is particularly challenging due to complex material behavior and multiple damage sources. While deep learning models offer potential, their application is limited by the lack of run-to-failure data and ground-truth HI labels. To address these challenges, this study proposes a novel approach that divides HI modeling into two tasks: time-independent (spatial) and time-dependent (temporal). This separation allows more effective data utilization, especially in the time-independent case. A semi-supervised spatial model is first developed and fine-tuned using a Bayesian algorithm with a coupled physics-based loss function that integrates both prognostic criteria and simulated labels—explicitly through the former and implicitly through the latter—embedding degradation physics into training. The study also introduces a new adaptive standardization technique for fatigue-based SHM and systematically evaluates principal component analysis (PCA)-based methods for dimensionality reduction prior to spatial and temporal modeling, simplifying subsequent network architectures. In the final stage, following time-based resampling, a semi-supervised temporal model captures HI evolution, with ensemble learning enhancing robustness. Validation on single-stiffener composite panels under fatigue loading, monitored via acoustic emission sensors, confirms the framework's generalizability and performance—achieving up to 90% (±2%) accuracy in prognostic metrics.