The loss of magnetic bearing in the process of operation will lead to the temperature rise of the bearing and affect its performance. A permanent magnet is used to provide bias magnetic flux for hybrid magnetic bearing, which can reduce the loss and temperature rise of the magnetic bearing. In this paper, the loss of radial 2-DOF hybrid magnetic bearing (HMB) is analyzed. On this basis, the 3D thermal analysis model of HMB is constructed by using ANSYS Workbench finite element software. The loss is introduced into the temperature field as a heat source, and the temperature distribution of magnetic bearing is calculated. Combined with the results of loss and temperature analysis, the structural parameters were optimized by using genetic particle swarm optimization algorithm (GAPSO). The results show that the loss and temperature rise of the optimized magnetic bearing are significantly reduced.
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