Academic Journal of Engineering and Technology Science, 2020, 3(7); doi: 10.25236/AJETS.2020.030707.
Ying Tan, Xiaohui Wang*, Guangzhou Shui
School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China
* Corresponding Author
A bearing fault diagnosis method based on Hilbert-Huang transform and matrix singular value is proposed. Make Hilbert-Huang transform (HHT) on bearing signals to obtain a spectrum of their 8 components. The sensitive mode functions which are used to reconstruct the signal are selected according to the characteristic frequency of the bearing. Make empirical mode decomposition which was improved by extension method again on the reconstructed signal, get 5 new intrinsic modal components (IMF). Then calculate their singular value de-composition (SVD) to get 5 Singular Values. At last inputting these 5 singular values as characteristic parameters into the Hyper-sphere Support Vector Machine (HSSVM) to learn classification, identify the type of failure of the rolling bearing, and use visualization technology to verify the effect. The experimental results show that the accuracy of the HSSVM classifier established by the method proposed in this paper is about 10% higher than that of the HSSVM classifier without parameter optimization, and the average response speed is increased by 3 seconds. It turns out that this pattern recognition method is advantageous both in terms of response speed and accuracy.
Hilbert-huang transform (hht), Singular value decomposition (svd), Support vector machine (svm), Pattern recognition
Ying Tan, Xiaohui Wang, Guangzhou Shui. Rolling Bearing Fault Pattern Recognition Method Based on Hht-Svd Parameter Optimization and Hssvm. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 7: 67-82. https://doi.org/10.25236/AJETS.2020.030707.
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