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以352226X2-2Z型货车滚动轴承为研究对象,通过小波包分析方法提取货车滚动轴承故障声发射信号特征向量,并将其作为神经网络的输入,通过概率神经网络(PNN)的模式识别功能实现故障类型分类,证明了小波神经网络在货车滚动轴承故障检测中的有效性。
Taking 352226X2-2Z rolling stock as the research object, the eigenvector of rolling mill bearing fault sound emission signal is extracted by wavelet packet analysis and used as the input of neural network to realize the fault type classification through the pattern recognition function of probabilistic neural network (PNN) , Which proves the effectiveness of wavelet neural network in fault detection of rolling stock bearing truck.