基于共振解调与神经网络的滚动轴承故障智能诊断

Intelligent diagnosis based on demodulated resonance technique and neural network for rolling bearing faults

介绍了一种基于共振解调与神经网络技术的滚动轴承故障诊断方法。对采集系统所拾取的滚动轴承振动信号进行共振解调处理,依据故障包络频谱中必然存在谐波谱线的规律,在共振解调后的包络信号中提取所需的轴承故障谱线特征信息,并将其作为神经网络输入,利用神经网络进行轴承各种故障状态的识别,实现滚动轴承故障的智能诊断。实验表明,该方法能准确而有效地识别出滚动轴承的不同磨损状态,诊断便捷。

This paper introduced rolling bearing fault diagnosis based on demodulated resonance technique and neural network. After demodulating resonance processing to roLling beating＇s vibrant signal which was got from the system of data acquisition, the authors can pick up the needing roRing fault information in the envelope signal based the law that the fault envelope spectrum have harmony wave spectrum. Input the fault information to neural network and identify aU kinds of fault state of the roiling bearing through neural network, which can implement the intelligent fault diagnosis of rolling bearings.