为诊断低速滚动轴承故障, 克服传统振动法诊断时故障信号极其微弱的缺陷。在实验室条件下对各类故障模式滚动轴承进行声信号采集, 并对故障轴承声信号进行参量分析和波形分析的基础上, 利用撞击数和神经网络技术对滚动轴承进行了故障诊断, 提高了低速滚动轴承故障诊断的有效性和准确性。

In order to diagnose the low speed fault rolling bearing and avoid extremely weak signals, it is suggested to use traditional vibration examination method. AE inspection experiments to the different fault pattern of rolling bearings were carried under the laboratory condition. Then the AE signals obtained from rolling bearing were analyzed using parameters and wavelet. Eventually fault patterns of rolling bearing were synthetically judged using hits and neural networks. The validity and the accuracy of low speed bearings fault diagnosis can be enhanced.