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Using convolutional neural network for vibration fault diagnosis monitoring in machinery
Conference paper

Using convolutional neural network for vibration fault diagnosis monitoring in machinery

Chiao Wei Yeh and Rongshun Chen
Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018, pp.246-249
01/2019
DOI: https://doi.org/10.1109/AMCON.2018.8614967

Abstract

Convolutional neural network Deep learning Intelligent vibration fault diagnosis Vibration detection of bearing Biomedical Engineering Mechanics of Materials Safety Risk Reliability and Quality Management Monitoring Policy and Law Education Fluid Flow and Transfer Processes Industrial and Manufacturing Engineering
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.

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