基于小波包能量分布特征的齿轮故障诊断方法研究  

Research on Methods of Fault Diagnosis of Gears Based on Features of Energy Distribution of Wavelet Packet

作　　者：王二化[1] 颜鹏[1] 李昕 王晓杰[3] WANG Erhua;YAN Peng;LI Xin;WANG Xiaojie(Changzhou College of Information Technology,Changzhou Jiangsu 213164,China;Changzhou Science and Education City Modern Industrial Center,Changzhou Jiangsu 213164,China;Institute of Advanced Manufacturing Technology,Hefei Institute of Physical Science,Chinese Academy of Sciences,Changzhou Jiangsu 213164,China)

机构地区：[1]常州信息职业技术学院,江苏常州213164 [2]常州科教城现代工业中心,江苏常州213164 [3]中国科学院合肥物质科学研究院先进制造技术研究所,江苏常州213164

出　　处：《机床与液压》2020年第1期188-192,共5页Machine Tool & Hydraulics

基　　金：常州信息职业技术学院青年基金项目(CXZK2016007);常州市高技术重点实验室(CM20183004);江苏省青蓝工程中青年学术带头人;常州信息职业技术学院“1+1+1”协同培育工程建设项目。

摘　　要：齿轮的裂纹故障不仅影响机械系统的整体性能,还会导致机器损坏,因此,研究了齿轮裂纹长度的故障诊断方法。以多传感振动信号为研究对象,将小波包各个频段的能量比系数作为齿轮裂纹的故障特征,并通过改进的神经网络模型进行特征分类,实现齿轮裂纹长度的故障诊断。研究结果表明:所提出的故障诊断方法识别率高(97.5%),通用性好,能有效辨识不同工况下的齿轮故障。Crack fault of gears not only affects the overall performance of mechanical system, but also causes machine damage. Therefore, the fault diagnosis method of crack length of gears is researched. The multi-sensor vibration signal was taken as the research object, and the energy ratio coefficients of each frequency band of wavelet packet were taken as the fault features of the gear crack, and the fault diagnosis of the gear crack length was realized by classifying features of improved neural network model. The results show that the fault diagnosis method proposed has high recognition rate(97.5%) and good versatility, which can effectively identify gear faults under different working conditions.

关 键 词：齿轮箱 振动 故障诊断 小波包分解 

分 类 号：TH165.3[机械工程—机械制造及自动化]