工矿自动化2011，Vol.37Issue(2)：49-51,3.DOI:CNKI:32-1627/TP.20110124.1038.006
基于RBF神经网络和小波包的电动机故障诊断研究
Research of Fault Diagnosis for Motor Based on RBF Neural Network and Wavelet Packet
王凡重 1田幕琴1
作者信息
- 1. 太原理工大学电气与动力工程学院,山西太原030024
- 折叠
摘要
Abstract
In order to solve the problem that it is difficult to extract fault characteristic signals and make an accurate fault prediction exsisted in traditional fault diagnosis for motor, a diagnosis method of motor faults based on RBF neural network and wavelet packet was put forward. The method can extract energy of special frequency bands of vibration signals of typical faults of bearing, rotor and insulation of motor with wavelet package analysis technology, and serve the energy as a group of vector to be input of RBF network for diagnosing motor fault. The experiment and simulation results showed that the method is very effective to diagnose motor, which has positive significance to find early fault and maintain for motor.关键词
电动机/故障诊断/RBF神经网络/小波包分析/振动信号Key words
motor/ fault diagnosis/ RBF neural network/ wavelet packet analysis/ vibration signal分类
矿业与冶金引用本文复制引用
王凡重,田幕琴..基于RBF神经网络和小波包的电动机故障诊断研究[J].工矿自动化,2011,37(2):49-51,3.
