MATEC Web Conf.
Volume 355, 20222021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|Number of page(s)||8|
|Section||Computing Methods and Computer Application|
|Published online||12 January 2022|
Research on intelligent diagnostic techniques for rolling bearings based on unbalanced data sets
College of Power Engineering, Naval University of Engineering, Wuhan 430032, China
In this paper, based on the combination of comprehensive sampling and one-dimensional convolutional neural network, a bearing fault intelligent diagnosis technique is proposed for the classification of rolling bearing vibration data. At first, the fault data set is expanded by ADASYN method. Then, the data is cleaned up by Tomek link under sampling technique, the risk of overfitting caused by overlap of different classes is reduced and the data of different categories is more apparent, and finally the normal data set and fault data set after comprehensive sampling are classified by one-dimensional convolutional neural network algorithm. Compared with random forests and support vector machines, the results show that the method has a high accuracy in identifying classifications and can effectively solve the classification problem of unbalanced bearing data.
Key words: Unbalanced data set / Comprehensive sampling / Convolutional neural network / Rolling bearings / Fault diagnosis
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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