Abstract
Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and functional principal component analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm (GA)-assisted orthogonal matching pursuit (OMP) for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.
