Gearbox Fault Diagnosis based on GAF-inceptionResNet - Changwen Li; Peng Li; Hua Ding (Jixie chuandong)
In order to improve the accuracy of gearbox fault diagnosis and accurately express the health status of the gearbox， combined with deep learning algorithms， a GAF-inceptionResNet model for gear fault diagnosis is proposed. The model can directly take the original one-dimensional vibration signal after GAF transformation to form photos as the input of the model. Through the stem-block， residual inception， residual module and classification layer， the residual inception network can broaden the network depth and improve the training time and accuracy， the residual block uses identity mapping to greatly reduce the training difficulty of the model. Therefore， the model can effectively mine the information between the signal features and enhance the feature learning ability of the model， thereby improving accuracy and accurately determine the faults. The test results show that the model can achieve a fault diagnosis accuracy of 99.59%. It can effectively achieve good gearbox fault identification and classification.