多重分形的振动信号故障特征提取方法  

Fault Feature Extraction Method of Vibration Signals Based on Multi-Fractal

作　　者：李兆飞[1] 柴毅[1] 李华锋[1] 

机构地区：[1]重庆大学自动化学院,重庆400044

出　　处：《数据采集与处理》2013年第1期34-40,共7页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(60974090)资助项目;教育部博士点基金(102063720090013)资助项目;中央高校基本科研业务费专项资金(CDJXS10172205)资助项目

摘　　要：针对非线性振动信号故障特征提取问题,提出一种广义维数均值(MeanDq)联合多重分形谱参数(Δα和Δf)的动特征提取方法。首先分析了振动信号的多重分形特性,然后计算出MeanDq,Δα和Δf分别作为故障特征量,并将其应用于滚动轴承故障状态的检测。研究表明:MeanDq,Δα和Δf能够有效地反映滚动轴承振动信号的状态,并且特征量MeanDq和Δα较Δf具有更好的灵敏度。实践证明该方法在实际应用中切实可行。Considering fault feature extraction difficulty to the non-linear vibration signals, a feature extraction method is proposed based on the general dimension mean （MeanDq） and the parameters of singular spectrum （△a and △ f）. Firstly, characteristics of multi-fractal for the vibration signals are analyzed, then MeanDq, Aa and Af are calculated, respectively. Subsequently, they are used as fault characteristic values. Finally, fault feature extraction method is applied to fault detection for rolling bearing. The result shows that the state of the vibration signals for rolling bearing can be effectively identified by MeanDq, △a and △f together. Besides, MeanDq and Aa have a stronger sensibility than △f. Apparently, the example proves that the integrated method is feasible.

关 键 词：振动信号 广义维数均值 奇异谱 滚动轴承 故障特征提取 多重分形 

分 类 号：TH11[机械工程—机械设计及理论]