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Intelligent Hybrid Method Based on 1D-CNN and SVM Applied to Gear Fault Diagnosis
Vinícius Serra Vianna , SP , Brazil
Co-Authors: Vinícius Serra Vianna, Milton Dias Junior
Presenter: Vinícius Serra Vianna
The features of nonlinearity and non-stationarity in real systems are often difficult to be extracted. This paper focuses on developing a Convolutional Neural Network (CNN) to obtain features directly from the original vibration signals of a gearbox with different pinion conditions. Experimental data is used to show the efficiency of the presented method. Support Vector Machine (SVM) is utilized to classify feature sets extracted with 1D-CNN. The obtained results show that the features extracted in this method have excellent quality for fault classification without any additional feature selection.
gearbox, 1D-CNN, svm, Fault Detection