A new fault diagnosis of rolling bearing on FFT image coding and L-CNN

Abstract

To address the problems of low diagnostic accuracy and slow diagnostic speed of the convolutional neural network (CNN) fault diagnosis method in rolling bearing diagnosis, a new rolling bearing fault diagnosis method based on fast Fourier transform (FFT) image coding and lightweight-CNN (L-CNN) is proposed. The method is mainly divided into three stages: firstly, the original signal is reconstructed by noise reduction using a joint noise reduction method of complete ensemble empirical mode decomposition with adaptive noise, permutation entropy, and wavelet threshold denoise; then, the frequency spectra and phase spectra feature fusion data of the noise-reduced and reconstructed bearing vibration signals are obtained by FFT, the feature fusion data are encoded into a heat map, and the image coding data-set is fed into an improved L-CNN for fault diagnosis. Experiments were carried out using the Guangdong University of Petrochemical Technology bearing fault data-set and the Case Western Reserve University bearing fault data-set with diagnostic accuracies of 98.75% and 99%, respectively. The results demonstrate that the method can effectively classify bearing fault vibration signals with the advantages of a fast diagnosis, high accuracy, and good generalization ability.