Research on Motor Fault Diagnosis Based on Deep Residual Network
The research of motor fault diagnosis technology is of great practical significance for ensuring safe production,reducing mechanical failures and minimizing production losses.Aiming at the limitations of traditional machine learning fault diagnosis methods,the motor fault diagnosis method based on deep residual network(ResNet)is proposed.Firstly,the traditional current signal characterization analytical method is analyzed.Then,a deep ResNet fault diagnosis framework is established.Finally,a deep learning motor fault diagnosis model with feature adaptive extraction is established by designing three-phase current input strategies with different modes,which effectively extracts the fault depth features of the motor current signal and verifies the diagnostic effect through comparative experiments.The experimental results show that the accuracy of the method is higher than that of the traditional machine learning method.The research lays the foundation for the popularization and application of deep ResNet in the field of motor fault diagnosis.
万方数据
维普
