Vibration Analysis Via Neural Network Inverse Models to Determine Aircraft Engine Unbalance Condition

Xiao Hu
J. L. Vian
Donald C. Wunsch, Missouri University of Science and Technology
J. R. Slepski

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This paper describes the use of artificial neural networks (ANNs) with the vibration data from real flight tests for detecting engine health condition - mass imbalance herein. Order-tracking data, calculated from time series is used as the input to the neural networks to determine the amount and location of mass imbalance on aircraft engines. Several neural network methods, including multilayer perceptron (MLP), extended Kalman filter (EKF) and support vector machines (SVMs) are used in the neural network inverse model for the performance comparison. The promising performances are presented at the end.