Structural health monitoring using strain data faces a critical challenge: decoupling subtle structural degradation signatures from the dominant influence of operational loads. This paper introduces a novel methodology to address this by synergistically combining a custom health indicator (HI) with graph neural networks (GNNs). The proposed HI, derived from the cumulative absolute first derivative of strain over time, effectively isolates load-independent features indicative of damage progression. These features serve as input to our proposed GENConv with Edge Attributes (GENEA) model, a GNN that explicitly models the spatially distributed sensors as an interconnected network, leveraging spatial interdependencies and edge attribute information within the strain field to enhance damage assessment. This integrated approach enables accurate structural stiffness reduction estimation (diagnostics) and remaining useful life (RUL) prediction (prognostics). Applied to strain data from fatigue tests on representative aeronautical composite panels, the methodology is rigorously evaluated using Leave-One-Panel-Out cross-validation. The framework shows promising performance on unseen test data, although challenges in generalizing to out-of-distribution specimens were also identified, highlighting the importance of a diverse training set for real-world applicability. Experimental results confirm the framework’s superiority. The proposed GENEA model significantly outperforms both a fundamental multi-layer perceptron and a spatially aware convolutional neural network baseline, and successfully generalizes to an unseen panel with a different sensor count. This validates the benefits of using a tailored GNN framework to learn robust, geometrically invariant patterns from load-decoupled spatial strain data.

Maintenance decisions often involve choosing between replacement and repair. The shortage of essential replacement parts has led to increased exploration of repair methodologies. However, repairs are often imperfect, leading to additional uncertainties in predicting the component's future condition. Existing approaches in the literature for modeling imperfect repairs struggle when repair dynamics are unknown requiring a large amount of data to be reliable. Furthermore, current methods are very task-specific, which limits the optimization of maintenance planning of varying components. This research addresses these challenges by conceptualizing imperfect repair effects as a stochastic increase in Remaining Useful Life (RUL). An existing deep learning model extracts prognostic-related features that can be utilized by any prognostic model to estimate RUL based on sensor data. Then, the proposed imperfect repair model predicts the RUL increase post-repair. This method offers three key benefits: (i) proactive post-repair assessment for improved maintenance, (ii) a data-driven repair model compatible with existing prognostic models, and (iii) flexibility in adapting to different repair techniques. Evaluation of the proposed model is conducted through tension-tension fatigue experiments on aerospace-grade aluminium specimens subject to imperfect repair. Results demonstrate the model's ability to accurately estimate the post-repair stochastic RUL increase.

Prognostics and health management (PHM) is becoming increasingly important as engineering structures and systems grow more complex. Many of these systems lack accurate physical models to describe their degradation, especially in unpredictable scenarios. To meet safety regulations, robust prognostic models are needed to transform sensor data into reliable predictions about a system’s remaining useful life (RUL). This study presents the adaptive hidden semi-Markov model (AHSMM), a novel probabilistic approach that enhances RUL prediction accuracy, uncertainty quantification (UQ), and reliability assessment compared to a long short-term memory (LSTM) model. A key contribution is an in-house experimental campaign involving glass fiber-reinforced polymer specimens subjected to fatigue loading and multiple impact events at different locations and time intervals. Unlike traditional models that rely on data from similar damage histories, the AHSMM is trained exclusively on unimpacted specimens and tested on multiply impacted ones, showcasing its adaptability to previously unseen conditions. The study also introduces a new prognostic performance measure tailored to AHSMM and develops a conditional reliability analysis for both AHSMM and LSTM predictions. Results demonstrate that AHSMM consistently outperforms LSTM across all evaluation metrics. It achieves a 24% lower RMSE over the full lifetime and superior UQ, with an average coverage of 0.79 compared to 0.17 for LSTM. Conditional reliability analysis further shows that AHSMM provides more accurate and stable reliability estimates as data accumulates. By capturing the degradation process and adapting to evolving conditions, AHSMM strengthens prognostic robustness. This study highlights the need for robust PHM models that can handle real-world uncertainties and contribute to advancements in the aerospace, automotive, and defense industries.

The work presented here focuses on the structural health monitoring (SHM) of a foreign object damage (FOD) composite panel equipped with an innovative printed piezoelectric transducer network. The 3D woven composite FOD panel measures approximately 800 mm × 320 mm, is curved with a cross-sectional thickness varying from approximately 2 mm to 12 mm, and a stainless-steel leading edge is bonded at one of its sides. The core idea explored here is to rely on an innovative screen-printing technology to print a full piezoelectric transducer a flowing to successfully achieve SHM on such a complex composite structure. This work is being carried out within the European project MORPHO - H2020. After printing a 25 elements PZT network, a four points bending fatigue experimental campaign using the PZT network along with other sensor technologies (embedded optical fibres with FBG sensors and acoustic emission sensors) is carried out. This unique experimental campaign allows to generate data and will help to develop diagnostic and prognostic methodologies for remaining life estimation and SHM of the FOD panel. It is demonstrated here through impedance measurements that the printing process associated with the printed PZT transducers is highly repeatable thus validating its use at a larger industrial scale. Furthermore, the printed piezoelectric transducers a re shown to be able to detect foreign object impact and sense Lamb waves signals. This innovative printing technology for PZT transducers network is thus extremely promising. It is furthermore highly advantageous to use the printed transducers for SHM instead of regular ceramic ones as this technology is non-intrusive, add negligible weight, can be printed during the manufacturing process, and arrays of transducers ensure easy availability of another transducer in case of failure of one.