A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis

A fusion autoencoder that combines physics-informed and pattern-informed techniques, augmented with a Beta-Variational Autoencoder learning backbone to enhance the robustness of the model is proposed and a novel health index called the piecewise anomaly index is proposed that can detect and classify faults simultaneously.