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.

A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI's quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI's quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI's quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior.

Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However, discovering or designing a suitable HI for composite structures is a challenging task due to the inherent complexity of the evolution of damage events in such materials. Previous research has shown that data-driven models are efficient for accomplishing this goal. Large labeled datasets, however, are normally required, and the SHM data can only be labeled, respecting prognostic requirements, after a series of nominally identical structures are tested to failure. In this paper, a semi-supervised learning approach based on implicitly imposing prognostic criteria is adopted to design a novel HI suitable. To this end, single-stiffener composite panels were subjected to compression-compression fatigue loading and monitored using acoustic emission (AE). The AE data after signal processing and feature extraction were fused using a multi-layer LSTM neural network with criteria-based hypothetical targets to generate an intelligent HI. The results confirm the performance of the proposed scenario according to the prognostic criteria.