Resumen de Prediction method of motor magnetic field based on improved Linknet model

Liang Jin, Yuankai Liu, Qingxin Yang, Chuang Zhang, Suzhen Liu

• Purpose – Under the condition of small data set, a prediction model of motor magnetic field is established based on deep learning method. This paper aims to complete the magnetic field prediction quickly and accurately.

  Design/methodology/approach – An improved Linknet model is proposed to predict the motor magnetic field. This is a digital twin technology, which can predict the function values of other points according to the function values of typical sampling points. The results of magnetic field distribution are represented by color images. By predicting the pixels of the image, the corresponding magnetic field distribution is obtained. The model not only considers the correlation between pixels but also retains the spatial information in the original input image and can well learn the mapping relationship between motor structure and magnetic field.

  Findings – The model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation.

  Originality/value – Under the condition of small data set, the model can well learn the mapping relationship between motor structure and magnetic field, so as to complete the magnetic field prediction quickly and accurately.

  In the future, according to the characteristics of magnetic field distribution, it will lay a foundation for solving the problems of rapid optimization, real-time simulation and physical field control of electrical equipment.