摘要
在电动机故障诊断技术中,最能全面反映电动机运行状态的唯独有振动信号。因此,提出一种基于小波分析和BP神经网络的电动机故障诊断方法。首先该方法采用小波包分析对振动信号消噪滤波并计算频带能量,随后根据振动信号大小提取其能量特征值,并以此建立电动机故障诊断的BP神经网络模型,再以Matlab软件的仿真模块为平台,最终开发了雨刮电动机故障诊断的智能检测系统。试验表明该系统的建立能够提高雨刮电动机故障诊断的效率和准确性。
In the fault diagnosis technology of the motor,the vibration signals can fully reflect the running status of the motor.So presents a method of motor fault diagnosis based on wavelet analysis and neural network.It firstly uses the wavelet packet analysis to filter noise of vibration signal and calculate energy of frequency-band,secondly according to the size of the vibration signal to get energy value of vibration signal,and then as a basis for the model of motor fault diagnosis established based on BP neural network,developed intelligent inspection system of diagnosis of the wiper motor by platform of Matlab simulation module.Experiments show that establishing this system can improve efficiency and accuracy of fault diagnosis of wiper motor.
出处
《现代制造工程》
CSCD
北大核心
2012年第2期135-138,共4页
Modern Manufacturing Engineering
