基于GG模糊聚类的滚动轴承退化阶段划分研究  

Degradation condition division of rolling bearing based on gath-geva fuzzy clustering

作　　者：孙德建[1] 胡雄[1] 王冰 王微[1] 林积昶 SUN De-jian;HU Xiong;WANG Bing;WANG Wei;LIN Ji-chang(Logistics Engineering College,Shanghai Maritime University,Shanghai 201306,China;81 team,32145 troops,Xinxiang 453000,China)

机构地区：[1]上海海事大学物流工程学院,上海201306 [2]32145部队81分队,河南新乡453000

出　　处：《机电工程》2019年第11期1166-1171,共6页Journal of Mechanical & Electrical Engineering

基　　金：国家高技术研究发展计划(“863计划”)资助项目(2013AA041106)

摘　　要：针对滚动轴承退化特征提取以及性能退化阶段准确划分的问题,采用Logistic混沌映射,对谱熵在复杂度演化中的变化规律进行了研究。提出了一种基于均方根、谱熵、“弯曲时间参数”特征以及GG模糊聚类的滚动轴承退化阶段划分方法,并采用IMS轴承实验中心的滚动轴承全寿命试验数据进行了实例分析。研究结果表明:谱熵参数能够有效描述性能退化过程中的复杂度变化规律,对复杂度变化十分敏感,计算速度快;引入的Curved Time参数能够反映退化状态在时间尺度上的集聚特性,更符合机械设备的性能退化规律,因此GG模糊聚类方法能够实现对轴承等机械设备性能退化阶段的准确划分。Aiming at the problem of degradation degree description performance degradation stage division for rolling bearings,the variation pattern of spectrum entropy in complexity evolution was studied by using the Logistic chaos mapping sequence.A division method of rolling bearing degradation stages based on root mean square,spectral entropy,“bending time parameter”and gath-geva(GG)fuzzy clustering was proposed.The example analysis was carried out and the life test data from the IMS bearing test center.The results show that reflect the complexity evolution tendency is able to be reflected by spectrum entropy which has a advantage of sensitive to variation and fast calculation speed.The continuity of the same state on the time scale is able to be described by introduced Curved Time parameter which is more according to performance degradation pattern for mechanical equipments.The degradation conditions of mechanical equipment such as bearings can be divided accurately by GG fuzzy clustering.

关 键 词：谱熵 GG模糊聚类 滚动轴承 特征提取 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP806.3[自动化与计算机技术—检测技术与自动化装置]