基于小波神经网络的采煤机摇臂故障诊断的研究  

Study of Shearer Rocker Fault Diagnosis Based on Wavelet Neural Network

作　　者：付家才[1] 王洪远[1] 

机构地区：[1]黑龙江科技大学,哈尔滨150027

出　　处：《煤矿机械》2013年第10期243-245,共3页Coal Mine Machinery

摘　　要：针对采煤机机械系统故障信号诊断的问题,在小波分析和神经网络的基础上,采用了一种基于小波神经网络诊断采煤机摇臂故障的方法。根据摇臂振动的信号通过小波分析检测出信号奇异点和突变情况,利用小波基函数作为小波神经网络的激励函数对故障信号做进一步的诊断,判断出故障特点和程度。结果证明此方法在故障诊断中的诊断准确率较高。Considering the failure of mechanical system in shearers,this paper proposed a method based on wavelet neural network diagnostic shearer rocker arm failure. This method originates the wavelet analysis and neural network. According to the rocker arm vibration signal, we can obtain the singular point and mutation by wavelet analysis the signal .Then analysis the fault using wavelet basisfunction as the wavelet excitation function of the neural network to do further diagnostic fault signal to determine the failure characteristics and extent. The results prove that this method in fault diagnosisdiagnostic accuracy was better.

关 键 词：小波分析 神经网络 故障诊断 采煤机 

分 类 号：TB112[理学—数学]