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.
gearbox fault diagnosis wavelet packet decomposition t-distributed stochastic neighbor embedding
Jinwen Sun Chen Lu Jian Ma
School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing, 100191, China
国际会议
The 28th International Conference on Vibroengineering (第28届国际振动工程会议)
北京
英文
91-96
2017-10-19（万方平台首次上网日期，不代表论文的发表时间）