摘要
Abstract
In motor fault diagnosis technique, the detections of vibration and stator current frequency components are two main detecting means. This article discusses the detection method of the vibration fault signal. Because this signal is a non-stationary random signal, the fault signals often contain a lot of time-varying, burst properties, the traditional Fourier signal analysis can not effectively extract the motor fault characteristics, it is likely that the weak signal of the rich failure information is regarded as noise to be deleted. For this the wavelet packet transform is used to extract the fault characteristics of the signal information. The result obtained is taken as the neural network input signal, L-M neural network optimization method is used for training, and then, the BP network is used for fault recognition. It also uses the Matlab software to carry out the simulation. It confirms that the method is valid for the motor fault diagnosis and the diagnosis is accurate.关键词
故障诊断/小波变换/神经网络/电动机/振动信号Key words
fault diagnosis/ wavelet transform/ neural network/ motor/ vibration signal分类
信息技术与安全科学
