Fault Diagnosis on Journal Bearing Using Empirical Mode Decomposition

Proper analysis of bearing faults is a necessity as it increases their working life and helps us in predicting their modes of failure beforehand. In the current study, paper deal with collecting and analyzing the vibration data of different faults of a journal bearing and comparison of the results thus obtained with a healthy bearing. To realize this objective, the vibration data for each fault condition as well as the healthy bearing is collected from the experimental setup. Journal bearing used for this study are made of gun metal. The signal obtained is split into Intrinsic Mode Functions (IMEs) by Empirical Mode Decomposition (EMD) method. Instantaneous frequency is calculated to meet the mono-component requirement. This is followed by the Hilbert Spectral Analysis from which the peaks are obtained for instantaneous frequency. Various faults can be determined by comparing these peaks with the known data. Although, Hilbert Huang Transform is an empirical method, it is more adaptive and can be used for the feature extraction of discrete and continuous data. As this method is based on the localized time scale of the data, it is applicable to both non-linear and non-stationary processes. (C) 2017 Elsevier Ltd. All rights reserved.