Bearing Health Assessment Using Time Domain Analysis of Vibration Signal

Abstract

Objective: Bearing defects are the most frequently occurring fault in any electrical machine. In this perspective, this manuscript proposed a novel statistical time-domain approach utilizing the vibration signal to detect incipient faults of rolling-element bearing used in three-phase induction motor. Methodology: To detect bearing defects, six time-domain features (TDFs) namely Mean Value (µ), Peak, Root Mean Square (RMS), Crest Factor (CRF), Skewness (SKW) and Kurtosis (K) were extracted from the standard database of the vibration signal. The standard databases of vibration signals were taken from the publicly available datacenter website of Case Western Reserve University (CWRU) relating to healthy, inner raceway and ball defects of bearing. Initially, the mean and standard deviation analysis of each considered TDFs of vibration signals were performed to discriminate the health conditions of bearing. Then, the box or whisker plot method was applied to visualize the variation in each TDF in terms of median and interquartile range (IQR) value for better analysis of bearing defects. Finally, a new index parameter termed as bearing fault index (BFIT) was also computed and this parameter predicts the bearing defects based on the mean of all considered TDFs. Results: The results of the “mean±σ” analysis have depicted that all considered TDFs except µ feature are almost independent to operating loads, and have discerning potential to diagnose bearing defects. The computations of these TDFs are mathematically very simple. The box plot representation of TDFs of vibration databases have shown that peak, RMS, and skewness features outperforms to demarcate bearing health conditions in terms of median and IQR value. The results of quantitative analysis of BFIT parameter have shown that if the magnitude of this parameter is higher than 1.8 then bearing is supposed to be faulty at all operating loads of machine. Thus, the BFIT analysis of TDFs is more simple and reliable to discriminate the health conditions of bearing. As most of the available techniques rely on the multi-processing of vibration data that requires large processing time and complicated mathematical model, so the proposed method prove to be simple and reliable in identifying the incipient bearing defects.