Jinwen Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jinwen Sun has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Chen Lu
Hangzhou International Innovation Institute, Beihang University, Hangzhou, China
1 shared publication Jian Ma
1 School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
1 shared publication
Latest Publications
- Open Access
- 21 October 2017
- Published by JVE International Ltd. in Vibroengineering PROCEDIA
- Vol. 14, 91-96
A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis
The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and t -Distributed stochastic neighbor embedding ( t -SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, t -SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes.
- 21 October 2017
- Published by JVE International Ltd. in Vibroengineering PROCEDIA
- Vol. 14, 91-96
Open AccessA feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis
The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and t -Distributed stochastic neighbor embedding ( t -SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, t -SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes.