New automated machine learning based imbalanced learning method for fault diagnosis

被引：0

作者：

Sun C. ^{[1
]}

Wen L. ^{[2
]}

Li X. ^{[1
]}

Gao L. ^{[1
]}

Cong J. ^{[3
]}

机构：

[1] State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan

[2] School of Mechanical Engineering and Electronic Information, China University of Geoscience, Wuhan

[3] School of Mechanical Engineering, Shandong University of Technology, Zibo

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Automated machine learning; Bayesian optimization; Fault diagnosis; Imbalanced data;

D O I：

10.13196/j.cims.2021.10.008

中图分类号：

学科分类号：

摘要：

To improve the performance of fault diagnosis models in imbalance dataset, an automatic imbalance fault diagnosis method based on Bayesian optimization was proposed. A hierarchical multi-model configuration space was constructed to explored the combination selection of resampling and classifier with their hyperparameters in this configuration space. Then a Bayesian optimizer based on Tree-structured Parzen Estimator (TPE) was used to optimize model training procedure. After training, an optimal model in the configuration space was obtained. The optimal configuration model was used to evaluate the results on the test dataset. The proposed method was applied to University of California Irvine (UCI) imbalance dataset and rolling bearing dataset. Experiments evaluated classification improvement after optimization by setting multiple imbalance ratios. Comparison with random search method was also conducted. The results showed that the proposed method improved the model classification performance better in imbalance fault diagnosis dataset, and optimization process is more efficient. © 2021, Editorial Department of CIMS. All right reserved.

引用

页码：2837 / 2847

页数：10

共 27 条

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