小波分形算法在旋转机械振动信号特征提取中的应用  

Application of wavelet fractal algorithm to feature extraction of rotating machinery vibration signals

作　　者：何珊[1] 梁红梅[2] 蒋劲[1] 肖志怀[1] HE Shan LIANG Hongmei JIANG Jin XIAO Zhihuai(Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, Wuhan University, Wuhan 430072, China Changji Vocational and Technical College, Changji 831100, China)

机构地区：[1]武汉大学水力机械过渡过程教育部重点实验室,湖北武汉430072 [2]新疆昌吉职业技术学院,新疆昌吉831100

出　　处：《武汉大学学报（工学版）》2017年第1期129-136,共8页Engineering Journal of Wuhan University

基　　金：水利部948项目(编号:201321);国家自然科学基金项目(编号:51379160)

摘　　要：为克服传统分形理论不包括信号细节成分的缺陷,提出了一种基于小波分形算法的旋转机械故障特征提取算法.该算法将小波函数和分形维数2种故障诊断方法结合起来,既考虑信号细节成分,也注重其局部与整体的关系.利用转子试验台系统模拟了3种故障工况下的旋转机械振动信号,并分别用传统分形维数算法和所提出的小波分形算法对其进行了特征提取.结果表明,2种算法提取得到的特征均有良好效果,但小波分形算法具有较高的准确性,为准确提取旋转机械振动信号故障特征提供了一种快速有效的新方法.To overcome the shortcoming of traditional fractal theory＇s lacking of signal detail components, a fault diagnosis method based on wavelet fractal algorithm is brought up in this article. The algorithm combines wavelet function and fractal theory together, so it considers both the signal detail components and the relationship between local and whole. An experimental machinery system is adopted to simulate ro- tating machinery vibration signals under three different conditions~ and they are feature extracted by both traditional fractal algorithm and the proposed wavelet fractal algorithm. The results show that both algo- rithms are effective in feature extraction, while the wavelet fractal algorithm provides a more accurate fea- ture extraction of rotating machinery signals.

关 键 词：分形维数 关联维数 小波分形算法 旋转机械振动信号 

分 类 号：TK05[动力工程及工程热物理]