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针对滚动轴承振动信号复杂,故障类型难以识别的问题,提出基于小波神经网络技术的滚动轴承诊断的一种新方法。该方法根据滚动轴承振动信号的频域变化特征,采用小波包分析对其建立频域能量特征向量,利用径向基函数神经网络完成滚动轴承故障模式的识别。通过仿真和试验数据对比,证明了该方法是有效的。
Aiming at the problem that the vibration signal of rolling bearing is complex and the type of fault is difficult to identify, a new method of diagnosing rolling bearing based on wavelet neural network is proposed. According to the frequency domain vibration characteristics of rolling bearing vibration signals, wavelet packet analysis is used to establish the energy eigenvectors in the frequency domain. The radial basis function neural network is used to identify the fault modes of the rolling bearing. The simulation and experimental data show that the method is effective.