Growing interest in using Electromechanical Actuators to replace current hydraulic actuation methods on aircraft control surfaces has driven significant research in the area of prognostics and health management. Non-stationary speeds and loads in the course of controlling an aircraft surface make fault identification in EMAs difficult. This work presents a time-frequency analysis of EMA thrust bearing vibration signals using wavelet transforms. A lab sized EMA system is designed and fabricated to allow for quick and repeatable component replacement. Indentation faults from moderate and heavy loads are seeded in the thrust bearings and are then tested to generate data. An artificial neural network achieves 95% classification accuracy in a two class scenario using healthy and moderately spalled thrust bearings.

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