Feature Evaluation for Effective Bearing Prognostics

Abstract

Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. It is not uncommon to replace a defected/used bearing with a new one that has shorter remaining useful life than the defected one. Thus, the prognostics of bearing plays critical role for increased availability and reduced cost. Effective prognostics highly depends on the quality of the extracted features. Diagnostics is basically a classification problem, whereas prognostics is the process of forecasting the future health states. The quality of the features for classification has been studied thoroughly. However, the evaluation of the quality of features for prognostics is a relatively new problem. This article presents an evaluation method for the goodness of the features for prognostics and presents results on bearings run until failure in a laboratory environment. Copyright © 2012 John Wiley & Sons, Ltd.