Abstract

Prognostics and Health Management (PHM) plays a vital role in the reduction of maintenance as the fault can be identified prior due to condition monitoring and its remaining useful life is predicted based on the condition of the component. Fault prognosis and condition monitoring uses the previous data and predicts the remaining useful life (RUL) of the component using various regression/degradation model. Bearing is one of the most important part of any rotating mechanical machine, so it is important to monitor its condition to ensure the best utilization and identify its remaining life to reduce the downtime. In this paper, statistical features are calculated from the denoised vibration data on which monotonicity analysis is performed to select the feature. That are then used to train the degradation model and predict the RUL for the degrading bearings. The degradation model is validated using the FEMTO bearing dataset provided by the National Aeronautics and Space Administration.

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