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 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.
Highlights
As one of the most important machine components, gearboxes are extensively used in transmission design of many rotating machine
This study provides a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and -Distributed stochastic neighbor embedding ( -Stochastic Neighbor Embedding (SNE)) for gearbox fault diagnosis
Our contributions are summarized as follows: Firstly, we proposed an effective feature extraction method relying on WPD-SVD and time-domain analysis methods for gearboxes
Summary
As one of the most important machine components, gearboxes are extensively used in transmission design of many rotating machine. As for time-frequency analysis methods such as short-time Fourier transform (STFT) and empirical mode decomposition (EMD), they have been proven effective to extract features from nonlinear and non-stationary vibration signals [5]. Among these time-frequency techniques, WPD is one of the best tools since it has particular advantages for decomposing original signals into different frequency bands. Our contributions are summarized as follows: Firstly, we proposed an effective feature extraction method relying on WPD-SVD and time-domain analysis methods for gearboxes. A -SNE based dimensionality reduction method is employed to obtain the discriminable features, relying on which fault diagnosis can be realized with a RBF neural network model.
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