Authors :
Mengyi Wang
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/mryn34sm
DOI :
https://doi.org/10.5281/zenodo.8275869
Abstract :
In view of the limitations of SVM in
processing data and classification, a bearing fault
diagnosis method based on LMD support vector machine
is proposed. The parameter tuning of kernel function
directly affects bearing fault diagnosis efficiency. Seven
kernel functions are selected for parameter tuning
evaluation in this paper.In this paper, the signal is
decomposed into a series of PF components by the local
decomposition algorithm, and six components are
selected to form the eigenvector. Secondly, the
experimental data were randomly extracted and
combined as a training set and a test set to test the
prediction accuracy of seven kernel functions under
different penalty parameters. Finally, seven kernel
functions are evaluated by Frideman test, and the radial
basis kernel function have the best performance.
Keywords :
Support Vector Machine;Local Mean Decomposition;kernel function;Bearing fault diagnosis.
In view of the limitations of SVM in
processing data and classification, a bearing fault
diagnosis method based on LMD support vector machine
is proposed. The parameter tuning of kernel function
directly affects bearing fault diagnosis efficiency. Seven
kernel functions are selected for parameter tuning
evaluation in this paper.In this paper, the signal is
decomposed into a series of PF components by the local
decomposition algorithm, and six components are
selected to form the eigenvector. Secondly, the
experimental data were randomly extracted and
combined as a training set and a test set to test the
prediction accuracy of seven kernel functions under
different penalty parameters. Finally, seven kernel
functions are evaluated by Frideman test, and the radial
basis kernel function have the best performance.
Keywords :
Support Vector Machine;Local Mean Decomposition;kernel function;Bearing fault diagnosis.
