Methodology and framework for predicting rolling element helicopter bearing failure

The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement with regards to 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, considering the limited number of run-to-failure data sets, the maturation of the prognostic techniques has not been achieved. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearing. 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 provides a framework for including prediction algorithms into existing health and usage monitoring systems. An oil-cooler bearing test-rig constructed by Impact Technologies LLC is used to facilitate the development of the remaining life prediction techniques. Two data sets are used in this study, in which both bearings experienced an inner race spall that progressed until the test was stopped due to an unsafe vibration level. The robust regression curve fitting results are promising in that the actual and predicted remaining life estimates converge for the run-to-failure oil-cooler bearing data sets a few hours prior to the stopping of the test. 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.