Anomaly Detection Strategies for Health-and-Usage Monitoring Systems in Helicopters' Transmissions

Helicopters are complex and vulnerable due to single-load-path critical parts that transmit the engine's power to the rotors. A fault in even one single transmission's gear component may compromise the whole helicopter, involving high maintenance costs and safety hazards. In this work, we present an effective diagnosis and monitoring system for the early detection of the mechanical degradation in such components, also capable of providing insights on the damage's causes. The classification task is performed by an ensemble of two learners: a convolutional autoencoder and a distance&density-based unsupervised classifier that use as regressors specific Health Indexes (HIs) and flight parameters. The proposed approach leverages the autoencoder reconstruction error information to infer the most probable cause of each detected fault, and enacts post-processing filtering policies defined to reduce the number of false alarms. Extensive experimental validation witnesses the effectiveness and robustness of the proposed approach.

Automated summary

This work presents an effective diagnosis and monitoring system for early detection of mechanical degradation in critical helicopter components. It utilizes a convolutional autoencoder and an unsupervised classifier to classify faults based on specific health indexes and flight parameters. The proposed approach leverages reconstruction error information to determine the most probable cause of faults and reduces false alarms through post-processing filtering.