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<p>The support bearing of permanent magnet synchronous motor (PMSM) is a key component of permanent magnet direct-drive system (PMDDS), and its reliability plays a critical role in the operational efficiency and safety of PMDDS. Existing PMSM bearing studies typically rely on static assumptions, neglecting time-varying reliability and degradation modeling. This study comprehensively considers the effects of electromechanical coupling and hybrid eccentricity of the PMSM rotor, investigates the evolution of bearing dynamic loads under varying system parameters and operating conditions, establishes a reliability assessment model for PMSM bearings, and proposes a dynamic reliability evaluation and sensitivity analysis method based on the active learning kriging (ALK) method. The results show that the proposed method greatly reduces calls to the actual performance function and provides highly accurate prediction results. Furthermore, the simulation results reveal the effects of various system parameters on the dynamic reliability of PMSM bearing. This study can provide a reference for the time-varying reliability prediction of PMSM bearings and the optimized design of PMDDS.</p>

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Dynamic reliability analysis of motor bearing in permanent magnet direct-drive system

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Liangjun Wu,
  • Wei Li,
  • Yuqiao Wang,
  • Song Jiang,
  • Lianchao Sheng,
  • Xuefeng Yang

摘要

The support bearing of permanent magnet synchronous motor (PMSM) is a key component of permanent magnet direct-drive system (PMDDS), and its reliability plays a critical role in the operational efficiency and safety of PMDDS. Existing PMSM bearing studies typically rely on static assumptions, neglecting time-varying reliability and degradation modeling. This study comprehensively considers the effects of electromechanical coupling and hybrid eccentricity of the PMSM rotor, investigates the evolution of bearing dynamic loads under varying system parameters and operating conditions, establishes a reliability assessment model for PMSM bearings, and proposes a dynamic reliability evaluation and sensitivity analysis method based on the active learning kriging (ALK) method. The results show that the proposed method greatly reduces calls to the actual performance function and provides highly accurate prediction results. Furthermore, the simulation results reveal the effects of various system parameters on the dynamic reliability of PMSM bearing. This study can provide a reference for the time-varying reliability prediction of PMSM bearings and the optimized design of PMDDS.