Dexin Gong

The analysis and calculation of the armature electromagnetic force is the premise of studying the dynamic characteristics of the electromagnetic railgun. Aiming at the problem of the numerical solution “pseudo-oscillation” at high speed, an extrapolation prediction method of armature electromagnetic force based on the Deep Belief Network-Deep Neural Network (DBN-DNN) is proposed. Firstly, the electromagnetic field control equation and armature dynamics equation, considering the influence of armature movement, are given, and the finite element simulation model of the electromagnetic railgun is established to analyze the dynamic characteristics and numerical solution stability of the armature electromagnetic force. Then, based on the stable numerical simulation data under different armature conductivities, a DBN-DNN method is proposed to realize the extrapolation prediction of the armature electromagnetic force under the standard conductance. Finally, the extrapolation prediction performance of the proposed method is tested by two electromagnetic railgun cases. Additionally, we further propose the training strategy of DBN-DNN parameters from solving armature electromagnetic force at low conductivity to standard conductivity. The armature electromagnetic force extrapolation prediction method for the whole launch process from low speed to high speed provides a new idea for the dynamic characteristic analysis of the high-speed electromagnetic railgun.

Using deep learning algorithm to establish agent model can realize rapid prediction and analysis of electrical equipment performance, but its training process requires a lot of data. However, the lack of labeled data of modern electrical equipment leads to the low accuracy of the trained deep learning model in predicting the performance of equipment, which restricts the engineering application of the algorithm. This paper establishes a deep transfer learning performance prediction method, which applies the transfer of performance prediction knowledge accumulated in the historical motor data to the performance analysis of the target motor, and completes the performance prediction task of the target motor with less labeled data and computational power consumption.