Sparse measure of bearing fault features based on Legendre wavelet multi-scale multi-mode Entropy

Abstract

The efficient diagnosis of the bearing fault categories is vitally significant to enhance the overall safety of rotating machinery and decrease the maintenance cost. This paper introduces an innovative sparse measure approach for the bearing fault characteristics using Legendre wavelet multi-scale multi-mode (LWMSMM) frequency domain. The motivation behind this approach lies in the need for an effective method that overcomes complexities associated with extracting transient characteristics and frequency-related bearing fault features. The core principle of the proposed method is to leverage information Entropy (IE) to pick up the most sensitive fault features and utilize the genetic algorithm (GA) to optimize the control parameters of LWMSMM frame, including the decomposition level and the number of wavelet base, so as to thoroughly match the more salient fault characteristics of the bearing. The essence of the effectiveness of the method lies in two aspects. One is that the rich properties especial diverse regularities of LW can effectively approximate the complex fault characteristics. The other is that IE can effectively represent the dynamic characteristics of the faults at each resolution scale and each mode frequency domain. Finally, experiments are conducted on three datasets to validate the effectiveness and robustness of the presented method. The experimental results demonstrate that the developed method can accurately identify different fault categories with the simpler classifiers and achieves a diagnosis accuracy of 100 %, surpassing cutting-edge approaches, and it provides a new and promising method for rotating machinery in real industrial applications.