An Extended Convolutional Neural Network with Smaller Structure for Fault Diagnosis of Gearbox

Abstract

Recent research on fault diagnosis mainly focuses on how to improve diagnostic accuracy. For a given accuracy level, a variety of convolutional neural network architectures have been developed and available to achieve the specific accuracy level. With equivalent fault diagnosis accuracy, smaller convolutional neural network structure offers at least three advantages: (1) it can be deployed on hardware with limited memory, such as FPGAs; (2) the training of smaller convolutional neural networks can be faster under the same processor performance; (3) smaller network structures require less communication across severs during distributed training. So, in this paper, an improved convolutional neural network is constructed, and the following strategies are used to reduce network parameters and further make the convolutional neural networks with smaller structures: (1) equivalent replacement of large-scale convolution layers by multiple small-sized convolution layers; (2) avoid using a fully connected layer, and replace with a global pooling layer. Meanwhile, to ensure the model’s fault diagnosis accuracy and training effectiveness, inspired by the network in network, a 1*1 convolutional layer is inserted into a traditional convolutional layer to improve feature expression, and the batch-normalization layer is used to increase the training effectiveness.