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Fault feature selection method for rolling bearings with multidimensional information fusion judging method

Published:2023-08-14 author:WU Bin-xin, LIU Mei, ZHOU Zheng-nan, et al. Browse: 355 Check PDF documents

Fault feature selection method for rolling bearings with multidimensional

information fusion judging method

WU Bin-xin1,2, LIU Mei2, ZHOU Zheng-nan1,2, WU Meng1, ZHANG Fei3,4

(1.College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China; 2.School of

Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China; 3.School of Mechanical Engineering,

Dongguan University of Technology, Dongguan 523419, China; 4.Hunan Provincial Key Laboratory of Health Maintenance for

Mechanical Equipment, Xiangtan 411100, China)

Abstract: A bearing fault feature selection method with a multidimensional information fusion judging approach was proposed to address the high time complexity of highdimensional data and data redundancy with multisensing data streams and fault feature construction. Firstly, the random forest and Spearman correlation analysis were used as the base point, and combining with gate recurrent unit (GRU) and auto regressive integrated moving average (ARIMA) model, the preliminary evaluation of each feature was made. Then, a new evaluation function was introduced to fuse the preliminary evaluation information of each part, eliminate the tail features and iterate one by one. A subset of features with low redundancy and better classification effect was selected. Finally, the proposed method was compared with recursive feature elimination based on random forest (RFE-RF)and maximum-relevance minimum-redundancy (mRMR) using Case Western Reserve University bearing data as an example, and the classification accuracy was used as an evaluation index to validate the model effect. The research results show that the method can obtain fewer feature subsets and improve computational efficiency while maintaining an accuracy of 97.5%. The model can provide reference for the accurate selection of rolling bearing fault characteristics.

Key words: data redundancy; random forest; correlation analysis; gate recurrent unit (GRU); information-fusion; bearing data

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