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The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement in the safety, maintainability, and reliability of a helicopter fleet. Current existing helicopter health and usage monitoring systems provide diagnostic information that indicates when the condition of a drive train component is degraded; however, prediction techniques are not currently used. Although various algorithms exist for providing remaining life predictions, prognostic techniques have not fully matured. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearings. The paper proposes a general methodology of how to perform rolling element bearing prognostics, and presents the results using a robust regression curve fitting approach. The proposed methodology includes a series of processing steps prior to the prediction routine, including feature extraction, feature selection, and health assessment. This approach provides a framework for including prediction algorithms into existing health and usage monitoring systems. A case study with the data collected by Impact Technology, LLC. is analysed using the proposed methodology. Future work would consider using the same methodology, but comparing the accuracy of this prediction method with Bayesian filtering techniques, usage based methods, and other time series prediction methods.