小波神经网络在齿轮早期故障诊断中的应用

Application of wavelet-neural networks to diagnosis of incipient gear fault

在单级齿轮传动运行试验的基础上 ,对齿轮箱表面振动进行同步信号平均采样处理 ,用小波分析来提取齿轮传动的故障信息特征 ,并以此作为前置处理手段为神经网络提供输入特征向量 ,利用神经网络的模式分类功能 ,有效地识别出正常齿轮传动、具有轮齿裂纹的齿轮传动和具有齿面剥落的齿轮传动

Firstly in the paper,the vibration from surface of gearbox is processed by the synchronous average sampling technique based on test of geared system running.Then the features of gear fault information are extracted by wavelet compact and entered into the neural networks as the input characteristic vectors.Three kinds of different states of gear running,i.e.the normal condition,the condition of tooth crack and the condition of tooth surface spalling,can be identified effectively by use of function of pattern identification of neural networks.