Application of Deep Support Vector Machine in Gear Fault Diagnosis - Lei Yu; Sen Chen; Rui Zhang; Ke Li; Lei Su (Jixie chuandong)
Gearbox fault diagnosis has problems in early feature extraction of non-stationary weak fault signals, vulnerability to strong background noise, and low accuracy of fault diagnosis. A gearbox fault diagnosis method based on Variational Mode Decomposition（VMD）and Deep Support Vector Machine(DSVM) is proposed. Firstly, the original vibration signal is decomposed into several frequency scale Intrinsic Mode Function (IMF) components by VMD, and the IMF component is selected according to the maximum kurtosis criterion to reconstruct the signal. Secondly, the multi-layer support vector is constructed. The SVM is used to train the training sample on the input layer, and it learns the shallow features of the data. The feature extraction formula is used to generate a new expression of the sample, which is used as input of the hidden layer. The hidden layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer, eventually, it outputs the diagnostic results on the output layer. The effectiveness of the proposed method is verified by the gearbox fault diagnosis experiment.