One-Dimensional Convolutional Neural Networks Based on Exponential Linear Units for Bearing Fault Diagnosis

Rolling bearings are one of the most commonly used components in rotating machinery which is mainly operated in complex working environment. Therefore, it is of great theoretical value and practical significance to study the state monitoring and fault diagnosis technology of rolling bearing to avoid sudden accidents and make a better system maintenance. In this paper, we propose a one-dimensional convolutional neural network to identify rolling bearing fault. Furthermore, we adopt a novel activation function: exponential linear units in the task of rolling bearing fault diagnosis. Simulation results show that one-dimensional convolutional neural network has a prominent generalization ability and high accuracy rate. Exponential linear units can make neural network more robust and stable when we diagnose the rolling bearing fault.