Torque/Speed Equilibrium Point Monitoring of an Aircraft Hybrid Electric Propulsion System Through Accelerometric Signal Processing

The present work proposes a new torque/speed equilibrium point monitoring technique for an aircraft Hybrid Electric Propulsion System (HEPS) through an accelerometric-signal-based approach. Sampled signals were processed using statistical indexes, filtering, and a feature reduction and selection algorithm to train a classification Feedforward Neural Network. A supervised Machine Learning model was developed to classify the HEPS operating modes characterized by an Internal Combustion Engine as a single propulsor or by combining the latter with an Electric Machine used as a motor or a generator. The abnormal changes in the torque/speed equilibrium point were detected by the monitoring index built by computing the Root Mean Square on the value identified by the classifier. The procedure was validated through experimental tests that demonstrated its validity.