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This study presents a new method based on convolutional neural network (CNN) for the gearbox fault identification and classification, which does not need the complex feature extraction process as those traditional recognition algorithms do, and it also depress the uncertainty of arbitrary feature selection. The vibration signals of the gearbox under normal and hybrid fault conditions were collected,...
A novel method is presented in this paper, which using Density-adjustable Spectral Clustering and Transductive Support Vector Machine, called DSTSVM, to accomplish feature extraction and fault detection. Firstly, the features are extracted via Density-adjustable Spectral clustering, and the Kernel function of TSVM (Transductive Support Vector Machine) is also constructed. Then the TSVM is trained...
Many intelligent learning methods have been successfully applied in the gearbox fault diagnosis. Self-organizing map (SOM) is one of such learning methods which have been used effectively as it preserves the topological relationships of the data. A novel distance preserving SOM is investigated in mechanical fault diagnosis, and a LDA-DPSOM (linear discrimination analysis and distance preserving SOM)...
To deal with pattern classification of complicated mechanical faults, an approach to multi-faults classification based on generalized discriminant analysis is presented. Compared with linear discriminant analysis (LDA), generalized discriminant analysis (GDA), one of nonlinear discriminant analysis methods, is more suitable for classifying the linear non-separable problem. The connection and difference...
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