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

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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，论文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。论文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，将文中提出的新指标与现有轴心轨迹特征进行对比，结果表明本文提出的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。

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	温广瑞1
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	张志芬1
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关键词 ：转子, 轴心轨迹, 象限信息熵, 模糊C均值聚类

Abstract：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.

Key words： Rotor Axis Orbit Information Quadrants Entropy Fuzzy C-Means Clustering

收稿日期: 2017-12-19 出版日期: 2019-07-28

引用本文:

温广瑞1,2,3，陈征1,2，张志芬1,2. 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[J]. 振动与冲击, 2019, 38(15): 27-35.
WEN Guangrui1,2,3,CHEN Zheng1,2,ZHANG Zhifen1,2. Rotor state diagnosis based on fuzzy C-mean value clustering and its axial center orbit features. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(15): 27-35.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I15/27

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