Abstract: 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.

Key words: slewing bearing, life state recognition, fuzzy C-means, performance degeneration, fault diagnosis

摘要： 为了对回转支承的寿命状态进行研究,以保障机械设备的高效正常运行,提出一种将点密度和模糊C均值结合的算法对回转支承的寿命状态进行识别,解决了传统模糊C均值算法识别速度慢、临界点分类不准确的问题。利用自主研发的回转支承综合性能实验台对某型号回转支承进行全寿命疲劳实验,验证了所提算法的可行性。通过对比所提改进算法与传统模糊C均值算法的识别结果,表明改进算法能够更准确地识别出回转支承的不同运行状态,从而为实时维修奠定了基础。

关键词: 回转支承, 寿命状态识别, 模糊C均值, 性能退化, 故障诊断