Turnout fault diagnosis method based on GADF and 2D CNN-improved SVM
Aiming at the problem that the fault characteristics of turnout were not easy to extract and the accuracy rate of turnout fault diagnosis was low,a combination method of Gramian Angular Difference Fields(GADF)and two Dimensional Convolutional Neural Network(2D CNN)-improved Support Vector Machine(SVM)for turnout fault diagnosis was proposed.Firstly,combined with the actual application situation on site,the switch machine power curve of normal conversion and typical fault of turnout equipment was selected.The sample database of switch machine power curve was established.The GADF coding method was used to convert the one-dimensional switch machine power curve signal into a two-dimensional feature map with time correlation.The feature maps of 16×16,32×32 and 64×64 were selected respectively and the image data was extracted.Secondly,based on the LeNet-5 model,a 2D CNN network structure model was designed.The image data was input into the turnout fault feature extraction model based on 2D CNN.The feature indicators were extracted through the multi-layer convolution layer,pooling layer and full connection layer to establish the turnout fault diagnosis sample database.The experimental results show that the power curve data of the switch machine is converted into a 64×64 feature map by GADF coding,and the typical feature data of the turnout is extracted by 2D CNN model.Compared with other data processing methods,it has higher fault diagnosis accuracy and improves the real-time performance of fault diagnosis.The established turnout fault diagnosis sample database is input into the NGO-SVM turnout fault diagnosis model.The fault diagnosis accuracy is as high as 97.5%,which has better fault diagnosis performance than other fault diagnosis models.It can provide a new method for turnout fault diagnosis and has certain guiding significance for the daily maintenance of on-site turnout equipment.
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