基于小波包和IGA-BP神经网络的滚动轴承故障识别方法

Rolling Bearing Fault Identification Method Based on Wavelet Packet and IGA-BP Neural Network

为识别数控机床运行过程中滚动轴承的运行状态,提高滚动轴承的故障状态诊断正确率,提出了一种基于小波包分解的改进遗传算法优化BP神经网络的滚动轴承故障识别方法。以滚动轴承的4种故障状态为研究对象,通过小波包分解振动信号,得到敏感特征向量;针对BP神经网络的缺点,运用改进遗传算法优化BP神经网络的阈值和权值,实现最优训练,建立更精确的滚动轴承IGA-BP状态预测模型。结果表明:IGA-BP预测模型收敛速度更快,预测准确率更高,证明了所提方法的有效性。

In order to identify the running state of rolling bearings during the operation of CNC machine tools and improve the fault state diagnosis rate of rolling bearings,an improved genetic algorithm based on wavelet packet decomposition was proposed to optimize the BP neural network fault identification method.Taking four fault states of rolling bearing as the research object,the vibration signal was decomposed by using wavelet packet,and the sensitive feature vector was obtained.Aimed at the shortcomings of BP neural network,the improved genetic algorithm was used to optimize the threshold and weight of BP neural network.The optimal training was achieved,and a more accurate IGA-BP state prediction model of rolling bearings was established.The results show that the IGA-BP prediction model has a faster convergence rate and higher prediction accuracy the effectiveness of the proposed method is proved.