The Adaptive Acoustic Monitoring of Aircraft Engines

A self-organizing, self-adaptive neural network has been assessed for application in a system for the monitoring of aircraft engines by the variation in their sound emission during operation. Equally applicable to vibrations or to any multi-sensor data relating to engine or airframe condition, the main feature of the proposed system is its capacity to learn its input data set without intervention. Experiments on recorded engine data are described.