基于模糊C均值聚类和转子轴心轨迹特征的转子状态诊断  

Rotor state diagnosis based on fuzzy C-mean value clustering and its axial center orbit features

作　　者：温广瑞[1,2,3] 陈征 张志芬[1,2] WEN Guangrui;CHEN Zheng;ZHANG Zhifen(Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System,Xi’an Jiaotong University,Xi’an 710049,China;Research Institute of Diagnostics & Cybernetics,Xi’an Jiaotong University,Xi’an 710049,China;Mechanical Engineering College,Xinjiang University,Urumqi 830046,China)

机构地区：[1]西安交通大学现代设计与轴承转子系统教育部重点实验室,西安710049 [2]西安交通大学机械工程学院智能仪器与监测诊断研究所,西安710049 [3]新疆大学机械工程学院,乌鲁木齐830046

出　　处：《振动与冲击》2019年第15期27-35,共9页Journal of Vibration and Shock

基　　金：装备预研共用技术和领域基金(6140004030116JW08001);国家重点研发计划项目(2017YF0210504);国家自然科学基金(51775409;51421004;51365051)

摘　　要：针对现有轴心轨迹特征用于转子故障程度判别识别精度低、效果差的问题,提出一种基于轴心轨迹象限信息熵的轴心轨迹特征提取新方法。该方法将轴心轨迹按象限划分为四个区域,分别计算四个区域的信息熵作为故障特征,然后使用模糊聚类进行故障模式识别和故障程度判别。通过分析网格划分程度对于聚类效果的影响,确定了象限信息熵获取过程中关键参数的确定方法,进而通过聚类中心初始化,改善了模糊C均值算法聚类效果不稳定的问题。通过在实验台进行不同故障不同程度的故障模拟实验,将提出的新指标与现有轴心轨迹特征进行对比,结果表明该方法在识别效果和数据可视化方面表现卓著,为后期进行实时状态监测和故障精密诊断提供了新的思路。Aiming at the problem of using existing axial center orbit features to identify rotor fault level having lower recognition accuracy and poor effect, a new feature extraction approach for rotor axial center orbit was proposed based on rotor axial center orbit quadrant information entropy. With this method, rotor axial center orbit was divided into four ranges according to quadrants, the information entropy of each range was calculated, respectively and taken as fault features. Then the fuzzy clustering was applied to do fault pattern recognition and fault level one. Effects of mesh size on clustering effect were analyzed to judge the method for determining key parameters in process to acquire quadrant information entropy. The stability of the fuzzy C-mean value clustering was improved by initializing clustering center. Fault simulation tests with different patterns and different levels were performed on a test platform. New indexes proposed here were compared with existing rotor axial center orbit features. The results showed that the proposed approach has a remarkable performance in recognition effect and data visualization, and can provide a new idea for further real time state monitoring and fault accurate diagnosis.

关 键 词：转子 轴心轨迹 象限信息熵 模糊C均值聚类 

分 类 号：TH212[机械工程—机械制造及自动化] TH213.3