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

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.