Permutation entropy-based 2D feature extraction for bearing fault diagnosis

Abstract

Bearing fault diagnosis based on the classification of patterns of permutation entropy is presented in this paper. Patterns of permutation entropy are constructed by using non-uniform embedding of the vibration signal into a delay coordinate space with variable time lags. These patterns are interpreted, processed and classified by employing deep learning techniques based on convolutional neural networks. Computational experiments are used to compare the accuracy of classification with other methods and to demonstrate the efficacy of the presented early defect detection and classification method.