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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
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86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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E-mail:
meem_contribute@163.com
Abstract: In order to fully extract the feature information of nonlinear nonstationary bearing fault signals and improve the accuracy of bearing fault diagnosis, a bearing fault diagnosis algorithm based on variational mode decomposition (VMD) and refined composite multiscale mean distribution entropy (RCMMDE) was proposed. Firstly, the algorithm used VMD to decompose the vibration signal into multiple modal components. The effective modes were screened by evaluating the cross correlation between the original signal and the modal component signal, and the signal was reconstructed to achieve signal noise reduction. Then, the traditional coarse graining method was replaced by the fine compound mean. The RCMMDE method was used for extracting the multiscale entropy value of the reconstructed signal, and constituting the feature sample set. Finally, the whale optimization algorithm (WOA) was used for optimizing the hyperparameters of support vector machine(SVM), and the optimal fault detection model was obtained. The feature sample set was inputted into the WOA-SVM model for bearing fault diagnosis. The validity of the model was evaluated by experiments. The research results show that the accuracy of the proposed model can reach 99.67%,precision rate, recall and other performance indicators are respectively above 99%, and the model has strong robustness.
Key words: bearing fault diagnosis; variational mode decomposition (VMD); refined composite multiscale mean dispersion entropy (RCMMDE); whale optimization algorithm (WOA); support vector machine(SVM); optimizing the hyperparameters
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