基于改进模糊C均值的回转支承寿命状态识别

To research the life state of slewing bearing for keeping machine work efficiently and stably, an algorithm by combining point density function with Fuzzy C-Means (FCM) which improved classification accuracy and calculation speed was proposed. An experiment on the full life test of slewing bearing was conducted based on the test platform to verify the effectiveness of improved method. The traditional FCM was used for comparison, and the results indicated that the proposed method could recognize the life state of slewing bearing more accurately, which laid the foundation of real-time maintenance.