Fault Diagnosis of Rolling Bearing Based on Vibration Signal and Deep Learning Algorithm

DOI

10.2991/978-94-6463-821-9_37How to use a DOI?

Keywords

Rolling Bearing; Deep Learning; Neural Network; Fault Diagnosis

Abstract

In today’s rapidly developing industrial sector, mechanical equipment is also more and more biased towards automation, intelligence. In the process of industrial production, mechanical equipment will inevitably have some failure problems. In view of the traditional manual diagnosis of faults may be excessive consumption of human resources, this paper takes the rolling bearing faults, which are more common in mechanical equipment faults, as the object of research, and proposes a bearing fault diagnosis model based on convolutional neural network by studying the application of deep learning on the vibration signals. This integrated approach enhances diagnostic accuracy by combining vibration signal feature extraction with fault type classification. and enhances the accuracy of fault recognition by training the model. To strengthen the architecture’s generalization performance, this paper also proposes to add the method of long and short-term memory network, and study the fault diagnosis model of the dual path of convolutional neural network and long and short-term memory network, which enhances the generalizability, further improves the accuracy of fault recognition, and has a certain degree of applicability, which is convenient for the diagnosis and repair of faults in industrial production.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yimuran Aimaier
PY  - 2025
DA  - 2025/08/31
TI  - Fault Diagnosis of Rolling Bearing Based on Vibration Signal and Deep Learning Algorithm
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 347
EP  - 363
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_37
DO  - 10.2991/978-94-6463-821-9_37
ID  - Aimaier2025
ER  -