Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Abstract

Identification of localized faults in rolling element bearing (REB) frequently utilizes vibration-based pattern recognition (PR) methods. Time domain (TD) statistical features are often part of the diagnostic models. The extracted statistical values are, however, influenced by the fluctuations present in random vibration signals. These inaccurate values consequently affect the diagnostic capability of the supervised learning based classifiers. This study examines the sensitivity of TD features to signal fluctuations. Vibration data is acquired from different REBs containing localized faults using a test rig, and a central tendency (CT) based feature extraction (CTBFE) method is proposed. The CTBFE ensures the supply of reliable feature values to the PR models. The method selects the fault related appropriate portion of a vibration signal prior to extract TD features. Variety of classifiers is used to judge the effect of CTBFE method on their fault classification accuracies, which are enhanced considerably. The results are also compared with a similar sort of existing method, where the proposed method provides better results and feasibility for on-line applications.