A Dilated Convolution Neural Network for Gear Fault Diagnosis

The identification of gear faults is crucial for maintaining the mechanical performance of equipment in practical industrial applications. In the study, a unique fault diagnosis model making use of dilated convolution neural networks is proposed for the classification of gear faults through the analysis of vibration signals generated by gear systems. The proposed model leverages dilated convolutions to expand the receptive field size without adding more parameters. The features extracted from each convolutional channel are concatenated and fed into a classification layer. The proposed approach is evaluated on a publicly available gear fault dataset and outperforms other methods currently available in terms of classification accuracy while reducing the number of parameters and computational complexity. These findings demonstrate the effectiveness of the developed method in diagnosing gear faults and highlight its potential as a promising approach for practical industrial applications.