国际设备工程与管理：英文版2012，Vol.17Issue(2)：104-111,8.
Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor
Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor
RONG Ming-xing1
作者信息
- 1. Heilongjiang Institute of Science and Technology, Harbin 150027, P. R. China
- 折叠
摘要
Abstract
In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy关键词
BP神经网络/转子故障诊断/电机故障/小波变换/故障信号检测/故障特征提取/非平稳随机信号/故障诊断技术Key words
fault diagnosis/wavelet transform/neural networks/motor/vibration signal分类
计算机与自动化引用本文复制引用
RONG Ming-xing..Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor[J].国际设备工程与管理：英文版,2012,17(2):104-111,8.
