Research on intelligent diagnostic techniques for rolling bearings based on unbalanced data sets

MATEC Web of Conferences

Li Jun¹  Xing Zhikai¹  Liu Yongbao¹  Wang Qiang¹ 

[1] College of Power Engineering, Naval University of Engineering;

关键词: unbalanced data set;    comprehensive sampling;    convolutional neural network;    rolling bearings;    fault diagnosis;   

DOI  :  10.1051/matecconf/202235503034

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown