A Fault Diagnosis Method based on Improved Synthetic Minority Oversampling Technique and SVM for Unbalanced Data

Equipment usually breaks down suddenly and irregularly, so most of the data sets obtained for fault diagnosis have unbalanced characteristics, and the amount of data varies greatly from different fault types. In this paper, three problems in the application of synthetic minority oversampling techniq...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-01, Vol.1043 (5), p.52034
Main Authors: Han, MingHong, Wu, Yaman, Huang, Yunfeng, Wang, Yumin
Format: Article
Language:English
Subjects:
Algorithms
Fault diagnosis
Oversampling
Support vector machines
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Summary:Equipment usually breaks down suddenly and irregularly, so most of the data sets obtained for fault diagnosis have unbalanced characteristics, and the amount of data varies greatly from different fault types. In this paper, three problems in the application of synthetic minority oversampling technique (SMOTE) are studied, and the improved SMOTE algorithm combined with support vector machine (SVM) is proposed. The validity of the model is verified by CWRU bearing data compared with SVM and SMOTE+SVM methods, and the result of fault diagnosis is satisfactory.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1043/5/052034