基于小波包和BP神经网络的刚性罐道故障诊断

Fault diagnosis of rigid cage guide based on wavelet packet and BP neural network

针对现有刚性罐道故障诊断方法不能消除环境因素影响、接头故障识别率较低等问题,以提高罐道故障种类识别精度为目标,提出了基于小波包和BP神经网络的刚性罐道故障诊断方法。搭建了立井提升系统实验台,模拟台阶突起故障和罐道接头故障这2种典型的罐道故障,采集提升容器振动加速度信号;运用小波包分解对采集的信号进行能量分析并提取故障特征参数,将故障特征参数作为BP神经网络的输入,并选取新的测试样本检测神经网络的诊断效果。测试结果表明,基于小波包分析和BP神经网络的刚性罐道故障诊断方法具有较高的故障识别精度,置信度达到了0.91。

In view of problems that existing fault diagnosis methods of rigid cage guide could not eliminate influences of environmental factors and low recognition rate of joint faults,a method of fault diagnosis of rigid cage guide based on wavelet packet and BP neural network was proposed in order to improve accuracy of identification of fault types of rigid cage guide.Experimental platform of lifting system of vertical shaft was set up to simulate two typical fault types of rigid cage guide including step protrusion and joint failure,and vibration acceleration signal of lifting vessel was collected. Wavelet packet decomposition was applied to carry out energy analysis and extract fault characteristic parameters.The fault characteristic parameters were taken as input of BP neural network,and a new test sample was selected to detect diagnostic effect of the neural network.The experimental results show that the method has high accuracy of fault identification,and the confidence level reaches to 0.91.