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Identification of bearing ring groove defects based on RFE-BXGBoost algorithm

Published:2023-11-27 author:XU Kai, ZHANG Huifang. Browse: 957 Check PDF documents

Identification of bearing ring groove defects based on RFE-BXGBoost algorithm

XU Kai1, ZHANG Huifang2

(1.College of Numerical Control Technology, Xinxiang Vocational and Technical College, Xinxiang 453000, China;

2.Dean's Office, Xinxiang Vocational and Technical College, Xinxiang 453000, China)

Abstract: The bearing ring groove is an important part of bearings, and its service life is affected by surface defects. The bearing groove surface defects were difficult to identify, therefore an identification model named recursive feature elimination-Bayesian extreme gradient boosting tree (RFE-BXGBoost) was proposed for bearing ring groove surface defects. Firstly, feature derivatization was used to extract the features of the time, frequency, etc. The extreme gradient boosting tree (XGBoost) was used as a base learner for recursive feature elimination (RFE) to achieve feature selection and eliminate the redundant features from the original dataset. Then, according to the results of feature selection in the original dataset, the hyperparameters of XGBoost were used as variables to be optimized by using Bayesian optimization algorithm. Moreover, in order to decrease the variance of the model, random sampling with replacement was used for the XGBoost that had been optimized by the Bayesian optimization algorithm. Finally, the identified results were obtained by using voting method based on the predicted results of XGBoost under random sampling with replacement method. The RFE-BXGBoost was applied to the test dataset of bearing ring grooves. The experiment result shows that the surface defect recognition model based on RFEBXGBoost has high identification performance, its accuracy is 0.90, and its F1-score is 0.879. The RFE-BXGBoost has higher effectiveness compared with other popular algorithms such as adaptive boosting (Adaboost), random forest, gradient boosting decision tree. The result shows that the RFE-BXGBoost has certain reference value for identifying the surface defects of bearing ring groove.

Key words: rolling bearing; recursive feature elimination (RFE); extreme gradient boosting tree (XGBoost); the bearing ring groove; random sampling with replacement; ensemble model

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