Induction motors most often fail due to faults in the rolling element bearings. Sudden failures in a system result in long unscheduled downtimes, which cause huge economic losses. Prediction of imminent failures and estimation of the remaining useful life (RUL) of a bearing is essential for scheduling prior maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid prognostics technique for rolling element bearings that utilizes dynamic regression models, which are updated recursively, to estimate the evolving trend in a bearing’s health indicator. These models are then used to predict the future value of the bearing health indicator and estimate the RUL of the bearing. The proposed algorithm is tested on the bearing prognostics data from the Center for Intelligent Maintenance Systems (IMS). Experimental results demonstrate excellent prognostic performance and bearing’s RUL estimates within the specified tolerance bounds by effectively determining the time to start prediction (TSP) and dynamically calibrating the models to adopt to the evolving behavior of the bearing health indicator.
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