Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Abstract

Rolling element bearings are crucial, of high failure rate and easy damage parts of rotating machinery, and significantly affect safe and reliable production processes. Much more attention has been focused on fault diagnosis of rolling element bearings in recent years. This article presents a novel feature extraction scheme for the classification of multiple bearing faults. Multi-Scale Sample Entropy (M-SSampEn) is combined with Energy Moment (EM) to construct a time-domain Multi-Scale Sample Entropy-based Energy Moment (M-SSampEn-EM) feature extractor. The classifier model for the proposed fault classification system has been built using the Least Square Support Vector Machine (LS-SVM). The M-SSampEn-EM feature extractor is used to capture two-dimensional representative eigenvectors from multiple fault classes' bearing vibration data. Its separability performance is ensured by optimizing the feature-extraction parameters, including the Embedded Dimension and the Tolerance, etc. The LS-SVM classifier is also compared with two Neural Network (NN)-based classifiers, i.e., Radial Basis Function NN (RBFNN) and Probabilistic NN (PNN), to show it better generalization performance on bearing fault classification. The experimental study verifies the excellent capacity of the proposed approach in bearing fault classification.