Robust Prognostics for Composite Structures Facing Unforeseen Impacts

Prognostics and Health Management (PHM) has gained prominence in recent years due to the increasing complexity of engineering systems and structures. Many of these systems lack comprehensive physical models that accurately describe their degradation processes, and there is limited engineering experience regarding their real-world operational behavior. Consequently, ensuring that predefined safety standards are met throughout the operational lifecycle has become critical. To train prognostic models, it is essential to gather data that captures the degradation process accurately. However, varying operational conditions can significantly impact degradation, underscoring the need for robust prognostic models capable of adapting to different operational scenarios and unexpected events. This work presents a novel adaptive prognostics model, the Adaptive Hidden Semi-Markov Model (AHSMM), designed to provide reliable Remaining Useful Life (RUL) predictions for engineering systems and structures under diverse loading conditions. Specifically, acoustic emission (AE) data from glass fiber-reinforced polymer (GFRP) specimens subjected to fatigue loading were used to train the model. To assess the robustness of the AHSMM, GFRP specimens were tested under the same fatigue conditions, but with multiple impacts simulating real-world phenomena such as hail. The AHSMM demonstrated its robustness and predictive accuracy outperforming the standard Hidden Semi-Markov Model (HSMM).

Keywords: PHM, Prognostics, Adaptive hidden semi markov model, Remaining useful life, Composites.