分数阶BP神经网络滚动轴承故障诊断方法

Fault diagnosis method of rolling bearings based on fractional-order BP neural network

研究基于分数阶BP神经网络滚动轴承故障诊断的问题。通过对滚动轴承5种状态类型特征信号的提取,以更全面的方式反映滚动轴承的工作特性。特征信号经过归一化处理后作为神经网络的输入,滚动轴承的故障类型作为神经网络的输出。运用分数阶BP神经网络对滚动轴承进行状态监测和故障诊断,判断其属于哪种故障类型。相较于整数阶BP神经网络,分数阶BP神经网络精度更高,且能更快地达到误差要求。仿真实验结果表明,分数阶BP神经网络能准确获取滚动轴承的运行状态。

Rolling bearings fault diagnosis problem based on fractional-order BP neural network was researched.The working characteristics of rolling bearings was reflected in a more comprehensive way by extracting five state types characteristic signals.The characteristic signals were normalized and used as the input of neural network,the fault type of rolling bearing was used as the output of neural network.Condition monitoring and fault diagnosis were performed on rolling bearing by applying fractional-order BP neural network in order to determine which fault type it belonged to.Compared to integerorder BP neural network,the accuracy of fractional-order BP neural network was higher and met the error requirements faster.The simulation experiment results show that the fractional-order BP neural network can acquire running conditions of rolling bearings accurately.