《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Bearing fault diagnosis algorithm based on CNN and CatBoost

Published:2023-07-18 author:LU Xi-yao, ZHANG Cheng-bin, GAO Jun, et al. Browse: 391 Check PDF documents

Bearing fault diagnosis algorithm based on CNN and CatBoost

LU Xi-yao1, 2, ZHANG Cheng-bin1, GAO Jun1, XU Yan-ping1, SHAO Xing1

(1.College of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China;

2.College of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

Abstract: When using general diagnostic algorithms for fault ranking of rolling bearings, feature extraction of the data was required, and there were defects in feature extraction in terms of large amount of data and limited manual extraction and selection. Aiming at this problem, a hybrid classification model based on convolutional neural network (CNN) and CatBoost was proposed. Firstly, the pre-processed data extracted by CNN was fed into this model with the features extracted as the input quantity to extract the model parameters output after training. Then, the rolling bearing dataset was analyzed using CatBoost method to further investigate the effect of different learning models on the classification accuracy under the same dataset. Finally, by reducing the risk of overfitting and applying four correlation coefficient indicators, a comparative experiment was conducted to study the classification effect of CNN-CatBoost hybrid classification model on rolling bearing data. The results indicate that the average accuracy of the proposed method is over 98%, which validates the effectiveness of the proposed method; a small number of data training samples can produce better classification results for bearing fault data, and has better performance in the Case Western Reserve University(CWRU) public bearing dataset compared with a single deep learning model and some typical machine learning models.

Key words: convolutional neural network (CNN); CatBoost algorithm; fault feature extraction; fault classification accuracy; deep learning model; training time

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