一种有效的齿轮故障识别方法

针对齿轮故障特征信息往往被信号中的噪声淹没的问题,提出了一种基于谐波小波包、样本熵和灰色关联度的齿轮故障识别方法。首先,采用顺序形态滤波器,并结合实际选用最简单的直线结构元素,对实测齿轮振动信号进行顺序形态滤波降噪预处理。然后,采用谐波小波包将不同故障的齿轮振动信号分解到3层共8个频带上,并计算各频带的样本熵。最后,以样本熵为元素构造特征向量,通过计算标准故障模式特征向量与待识别样本的灰色关联度来判断齿轮的工作状态和故障类型。试验结果表明,该方法能够有效地应用于齿轮系统的故障诊断。

An effective identification method of gear fault

In view of problem that feature information of gear fault often submerged by noise in signal, an identification method of gear fault based on harmonic wavelet package,sample entropy and grey correlation degree was proposed.Firstly,the method uses rank-order morphological filter and selects the simplest line structure element combining reality to conduct denoising pretreatment with rank-order morphological filtering for actual measured gear vibration signals.Then,it uses harmonic wavelet package to decompose the gear vibration signals of different faults into eight frequency bands in three levels,and calculates sample entropy of each band.Finally,it takes the sample entropies as elements to construct feature vectors,and calculates grey correlation degree of feature vector of standard fault pattern and test sample to identify working state and fault type.The test result shows that the method can be applied to fault diagnosis of gear system effectively.