Using convolutional neural network for vibration fault diagnosis monitoring in machinery

Abstract

This work proposes an intelligent bearing fault diagnosis system using Convolutional Neural Network (CNN) in deep learning to achieve the abnormal identification of bearing vibration. In this system, the convolutional kernel in CNN can automatically extract the features of input signals and no human feature extraction and other data pre-processing are required. As a result, comparing to the traditional signal processing methods, this work has the advantages of automated end-to-end, high-accuracy and intelligent machine troubleshooting in vibration fault diagnosis of bearings.