轴承振动信号PF分量的分数低阶特征提取  

Fractional Lower Order Feature Extraction Method of PF Components of Rolling Bearings

作　　者：徐倩倩 林茂海[1,2] 刘凯[3] 赵文全[1,2] XU Qianqian;LIN Maohai;LIU Kai;ZHAO Wenquan(Key Laboratory of Green Printing&Packaging Materials and Technology,Qilu University of Technology(Shandong Academy of Sciences)Jinan,250353,China;School of Light Industry Science and Engineering,Qilu University of Technology(Shandong Academy of Sciences)Jinan,250353,China;School of Mechanical and Precision Instrument Engineering,Xi'an University of Technology Xi'an,710048,China)

机构地区：[1]齐鲁工业大学(山东省科学院)绿色印刷包装材料与技术重点实验室,济南250353 [2]齐鲁工业大学(山东省科学院)轻工科学与工程学院,济南250353 [3]西安理工大学机械与精密仪器工程学院,西安710048

出　　处：《振动．测试与诊断》2020年第6期1141-1149,1233,1234,共11页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51275406);陕西省自然科学基础研究计划资助项目(2017JQ5012)。

摘　　要：针对分数低阶Alpha稳定分布特性下轴承振动信号乘积函数(product functions,简称PF)的高阶统计特征提取性能退化问题,提出了轴承振动信号PF分量的分数低阶特征提取方法。通过信号平稳化PF分量的概率密度函数(probability density function,简称PDF)曲线拖尾及特征指数α估计,验证轴承振动信号PF分量的分数低阶Alpha分布特性,并提出最优分数低阶统计量和共变低维流行映射矩构成轴承特征矩阵,以降低二阶及高阶统计量对信号分量特征描述误差,实现不同故障轴承特性的准确性表征。通过轴承特征散点图对比分析,结果表明,分数低阶次特征对轴承振动信号PF分量描述更为准确,不同状态轴承特征描述准确性和区分效果提升明显,具有一定的可行性及实际应用优势。In light of the degradation of the feature extraction from the second or higher-order statistic in the context of Alpha stable noise,a new feature extraction method of PF(product functions)components is introduced.The distribution properties of PF are validated by the tails and the estimation ofαof the probability density function(PDF).Then,the bearing feature matrix is constructed based on the optimal fractional low-order statistics(FLOS)and covariant low-dimensional popular mapping matrix in order to reduce the error of the second-order and high-order statistics in describing the characteristics of signal components.Thus,various bearing faults are described accurately and intuitively.The comparisons with traditional methods show that lower-order features better describe the PF components with higher accuracy and clearer distinction.The feasibility indicates the advantages of the proposed method in practical applications.

关 键 词：ALPHA稳定分布 分数低阶次 共变 特征描述 

分 类 号：TH17[机械工程—机械制造及自动化]