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Rolling bearing fault diagnosis method based on CEEMD-PCA-XGBoost
Published:2023-04-20 author:MA Dong, HE Yi-bin, LI Ming, et al. Browse: 445 Check PDF documents
Rolling bearing fault diagnosis method based on CEEMD-PCA-XGBoost


MA Dong, HE Yi-bin, LI Ming, TANG Quan, HU Ming-tao

(College of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China)


Abstract: Aiming at the problems of slow operation speed and low classification accuracy due to data loss or uneven distribution in bearing fault diagnosis, a fault diagnosis method combined with complementary ensemble empirical mode decomposition and principal component analysis and extreme gradient boosting(CEEMD-PCA-XGBoost) was proposed. Firstly, based on complementary ensemble empirical mode decomposition (CEEMD), the third-party bearing fault data sets were extracted in the time domain and frequency domain to achieve preliminary data screening. Then, the principal component analysis (PCA) method was used to reduce the eigenvalue dimension for the decomposed intrinsic mode functions (IMF).The extracted eigenvalues were input into the extreme gradient boosting (XGBoost) model as input quantities, and the model parameters were optimized by the raster method. Finally, the method was verified by two different bearing data sets, and the results obtained by this method were compared with those obtained by other algorithms from the perspective of classification precision and accuracy. The experiment results show that the classification precision of the optimized model is 100%, and the calculation time is 11.264 s, verified by the bearing dataset of Case Western Reserve University; verified by the IEEE PHM 2012 dataset, the fitting effect of the life prediction curve is better than other algorithms. The research results show that,the method has good comprehensive performance in terms of operation speed and classification accuracy.

Key words: feature extraction; complementary ensemble empirical mode decomposition(CEEMD); principal component analysis(PCA); extreme gradient boosting(XGBoost); classification accuracy; eigenvalue dimension

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