Gear fault diagnosis based on small channel convolutional neural network under multiscale fusion attention mechanism

Abstract

Due to the insufficient feature learning ability and the bloated network structure, the gear fault diagnosis methods based on traditional deep neural networks always suffer from poor diagnosis accuracy and low diagnosis efficiency. Therefore, a small channel convolutional neural network under the multiscale fusion attention mechanism (MSFAM-SCCNN) is proposed in this paper. First, a small channel convolutional neural network (SCCNN) model is constructed based on the framework of the traditional AlexNet model in order to lightweight the network structure and improve the learning efficiency. Then, a novel multiscale fusion attention mechanism (MSFAM) is embedded into the SCCNN model, which utilizes multiscale striped convolutional windows to extract key features from three dimensions, including temporal, spatial, and channel-wise, resulting in more precise feature mining. Finally, the performance of the MSFAM- SCCNN model is verified using the vibration data of tooth-broken gears obtained by a self-designed experimental bench of an ammunition supply and delivery system.