Research on Inter-Turn Short Circuit Fault Diagnosis of Electromechanical Actuator Based on Transfer Learning and VGG16

Abstract

In this paper, an inter-turn short-circuit fault of a permanent magnet synchronous motor in an electromechanical actuator is analyzed, and a fault diagnosis method based on transfer learning with a VGG16 convolution network is proposed. First, a 2D finite element model of an inter-turn short circuit fault of a permanent magnet synchronous motor was established in ANSOFT Maxwell, and then a simulation experiment analysis was completed. A three-phase current was chosen as a fault characteristic signal. Second, a fault diagnosis method with a VGG16 deep convolutional neural network and based on transfer learning was designed, and the fine tuning of the hyperparameters of the fault diagnosis model was completed by using grid search and cross verification methods. Finally, based on the transfer learning VGG16 model established in this paper, the inter-turn short circuit fault of a permanent magnetic synchronous machine (PMSM) was diagnosed and verified. The experimental results showed that the proposed convolutional network model based on transfer learning can identify faults effectively and accurately, and has a good engineering guidance significance.