A multi-level prognostic framework for delamination-induced failure under compressive fatigue

• Multi-level framework for predicting delamination-induced fatigue in composites • CNN, MLP, and LSTM models process raw and engineered guided-wave features • Framework improves RUL prediction by up to 11% vs best and 55% vs worst baselines • Framework improves RUL prediction by up to 11% and 55% over lowest- and highest-error single-frequency baselines. • Developed on impacted woven CFRP laminates under compressive fatigue loading Predicting the remaining useful life (RUL) of composite structures is particularly challenging in impact-damaged carbon fiber–reinforced polymers (CFRPs) under compressive fatigue, where delamination growth with complex morphology and stochastic progression often governs failure. Guided wave–based structural health monitoring (GW-SHM) enables sensitive damage characterization, yet RUL prediction remains difficult due to the strong dependence of GW–delamination interactions on excitation frequency and damage geometry. Physics-based models often struggle to generalize beyond specific configurations, whereas purely data-driven models can capture complex patterns but typically lack consistency with the underlying physical mechanisms. This study introduces a multi-level, frequency-aware prognostic framework that combines the adaptability of deep learning with the physical interpretability of engineered features. GW signals acquired at multiple excitation frequencies are transformed into time- and time–frequency representations, while damage indicators are derived through temporal segmentation. These indicators are correlated with delamination growth measured by C-scan inspections, providing a link between signal-derived features and physical damage evolution. The multi-level architecture integrates convolutional neural networks, multilayer perceptrons, and long short-term memory layers to capture complementary aspects of degradation. Experimental assessment on seven specimens demonstrates that the proposed framework achieves a minimum mean absolute percentage error (MAPE) of 1.904, corresponding to 11% and 55% improvements over the highest- and lowest-performing single-frequency baselines at 160 kHz and 100 kHz, respectively. The results confirm that integrating GW signal processing with deep learning yields robust and physically consistent RUL predictions for impact-damaged CFRPs, while enhancing the interpretability of prognostic outcomes.