A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis
Jinwen Sun1, Chen Lu2, Jian Ma3
School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing, 100191, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com
Abstract. 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 -Distributed stochastic neighbor embedding (-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, -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.
Keywords: gearbox, fault diagnosis, wavelet packet decomposition, t-distributed stochastic neighbor embedding.
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Cite this article
Sun Jinwen, Lu Chen, Ma Jian A feature fusion method using WPD‑SVD and t‑SNE for gearbox fault diagnosis. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 91‑96.
© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533