Application of feature reduction techniques for automatic bearing degradation assessment

Bearings are important assets for most industrial applications. The non-destructive diagnosis of these elements needs an accurate and reliable acquisition of its dynamic vibration signals affected by noise and the other part of system such as gears, shafts, etc. Empirical mode decomposition is an advanced signal processing tool for bearing fault feature extraction. In this paper, empirical mode decomposition is used to decompose non-linear and non-stationary bearing vibration signals into several stationary intrinsic mode functions and the empirical mode decomposition energy entropy is computed for each intrinsic mode function. Moreover, principal component analysis and linear discriminant analysis are used for feature reduction. Based on the Fisher's criterion, experimental results show that linear discriminant analysis features are highlighted compared to principal component analysis features and original empirical mode decomposition features for bearing fault diagnosis as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting).