The Review of Bearing Fault Diagnosis Technology Based on Machine Learning

Yi Jiang

doi:10.62177/jaet.v1i3.91

Authors

Yi Jiang Department of College of Information and Mechanical and Electrical Engineering, Hunan International Economics University

DOI:

https://doi.org/10.62177/jaet.v1i3.91

Keywords:

Bearing Fault Diagnosis, Machine Learning, Deep Learning, Convolutional Neural Network, Long Short-Term Memory Network

Abstract

This paper reviews the development and applications involving bearing fault diagnosis technology based on machine learning and deep learning and explores the limitations of traditional fault diagnosis methods and advantages of machine learning technology in improving diagnostic accuracy and efficiency. The study represents that these technologies can effectively extract and learn features from vibration signals for high-precision fault diagnosis through in-depth research into the application of models like support vector machines, convolutional neural networks, and long short-term memory networks. It also summarizes the deep learning research results in bearing fault diagnosis up to now, points out the superior performance of it in complex working conditions, and highlights its role in improving mechanical equipment reliability and safety, both played and to be played.

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