An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

Abstract

Due to the complicated structure and tough working environment of planetary gearboxes, intelligent identification of the health states based on the raw vibration signal is still a huge challenge in equipment maintenance. Aiming at this issue, an enhanced convolutional neural network (ECNN) with enlarged receptive fields was proposed in this paper. First, a one-dimensional convolutional layer was applied to enlarge receptive field preliminarily and capture the fault information within each group of adjacent points in the vibration signal. Then, several fused dilated convolutional layers were constructed to enlarge the receptive field further and capture the long distance dependencies of the raw signal comprehensively. At last, the raw vibration signals were directly fed into the developed ECNN to train the fault diagnosis model, and evaluate the ECNN model with the testing data. The experimental results demonstrated that the developed method can enhance the fault feature learning ability by enlarging the receptive fields twice, and achieved higher diagnosis accuracies than the traditional deep learning methods in fault diagnosis of planetary gearboxes.