Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features

Abstract

Time-domain (TD) statistical features are frequently utilized in vibration-based pattern recognition (PR) models to identify faults in rotating machinery. Presence of possible fluctuations or spikes in random vibration signals can considerably affect the statistical values of the extracted features consequently. This paper discusses the sensitivity of TD features against the fluctuations occurred in vibration signals while classifying localized faults in ball bearing. Based on the sensitivity level, the features are statistically processed prior to employing a classifier in PR model. A central tendency-based feature pre-processing technique is proposed that enhances the diagnostic capability of classifiers by providing appropriate values. The feature processing reduces undesired impact of fluctuations on the diagnostic model. Several classifiers are utilized to evaluate the performance of the proposed method, and the results are evident of its effectiveness. The associated advantage of the feature pre-processing over the conventional pre-processing of raw data is its computational efficiency. It is worth mentioning that only few values in feature distributions are required to be processed rather than dealing with big TD vibration data set.