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This research investigates a novel data-driven approach to condition monitoring of Electrical-Mechanical Actuators (EMAs) consisting of feature extraction and fault classification. The approach is designed to accommodate varying loads and speeds since EMAs typically operate under non-steady conditions. Since many common faults in rotating machinery produce unique frequency components, the approach is based on signal analysis in the frequency domain of both inherent EMA signals and accelerometers. The feature extraction process exposes fault frequencies in the signal data that are synchronous with motor position through a series of signal processing techniques consisting of digital re-sampling to the position domain, Power Spectral Density (PSD) computation to the frequency domain, and feature reduction. The reduced dimension feature is then used to determine the condition of the EMA with a trained Bayesian Classifier.