电测与仪表2018，Vol.55Issue(3)：55-58,4.
基于Hilbert模量与改进BP神经网络的电机转子断条故障诊断
Broken rotor bar fault diagnosis of induction motors based on Hilbert modulus and improved BP neural network
苟旭丹1
作者信息
- 1. 成都城电电力工程设计有限公司,成都610041
- 折叠
摘要
Abstract
In order to identify the broken rotor bar faults in induction motors accurately and rapidly,this paper illustrates a novel method to diagnose broken rotor bar fault on the basis of Hilbert modulus and BP neural network evolved by chaos particle swarm optimization (CPSO).Firstly,Hilbert modulus of stator current can transform the power frequency component into DC component to weaken the influence of the fundamental frequency signal in stator current,which can help extract the feature vector accurately.Compared with BP neural network,CPSO-BP neural network has superior initial weights and can strengthen the fault classification rate.As a result,the experiment reminds the effectiveness and superiority of the proposed method.关键词
转子断条/Hilbert模量/混沌粒子群/BP神经网络/故障诊断Key words
broken rotor bar/Hilbert modulus/chaos particle swarm optimization/BP neural network/fault diagnosis分类
信息技术与安全科学引用本文复制引用
苟旭丹..基于Hilbert模量与改进BP神经网络的电机转子断条故障诊断[J].电测与仪表,2018,55(3):55-58,4.
