Multiscale Dynamic Fusion Global Sparse Network for Gearbox Fault Diagnosis

Abstract

One-dimensional vibration signals are widely used for gearbox fault diagnosis to perform maintenance timely and then reduce various losses. The fault diagnosis accuracy of those classifiers is determined by the features extracted from the vibration signals. These typical deep neural networks (DNNs), e.g., convolutional neural network (CNN) and convolutional autoencoder (CAE) have been applied in machinery fault diagnosis. However, the feature learning of DNNs on the nonlinear vibration signals is still a big challenge for gearbox fault diagnosis. In this article, a new CNN, multiscale fusion global sparse network (MFGSNet) is proposed for feature extraction from vibration signals and gearbox fault diagnosis. First, a novel kernel dynamic fusion method based on multiscale convolution is proposed to extract defect features of vibration signals; second, a global dense connection method is proposed to generate features from shallow and deep layers; finally, a spare feature selection layer is embedded in the deep network to perform feature selection and dimension reduction on the shallow and deep features. The experimental results show that MFGSNet has good fault feature extraction performance on gearbox vibration signals. It performs much better on gearbox fault diagnosis than these typical DNNs, e.g., residual network (ResNet) and dense connection network (DenseNet).