Analysis of statistical features for fault detection in ball bearing

Abstract

Fault detection in ball bearing has attracted attention of various researchers. Several Statistical features have been proposed and used by various researchers for fault detection in ball bearing. This work analyzes the importance of various available statistical features by different methods which includes graphical analysis, feature ranking using information gain and gain ratio. The results show that some of the statistical features can be used individually to distinguish between healthy and faulty ball bearings, i.e. we just need to use one statistical feature for distinguishing healthy and faulty ball bearings instead of using an ensemble of features, which is generally the case. This paper also proposes a new metric, which ranks the features based on how well the statistical features distinguish between healthy and faulty ball bearings, to identity the importance of statistical features for identifying faults.