基于Hilbert模量与改进BP神经网络的电机转子断条故障诊断

苟旭丹

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.
GOU Xudan.Broken Rotor Bar Fault Diagnosis Based on Hilbert Modulus and Improved BP Neural Network in Induction Motors[J].Electrical Measurement & Instrumentation,2018,55(3):55-58.

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 Based on Hilbert Modulus and Improved BP Neural Network in Induction Motors

DOI：

中文关键词: 转子断条 Hilbert模量混沌粒子群 BP神经网络故障诊断

英文关键词:Broken rotor bar, Hilbert modulus, Chaos particle swarm optimization, BP neural network, Fault diagnosis

基金项目:

作者中文名	作者英文名	单位
苟旭丹	GOU Xudan	成都城电电力工程设计有限公司

摘要点击次数: 1685

中文摘要:

为了更加快速准确识别感应电机转子断条故障,本文提出一种基于定子电流Hilbert模量与混沌粒子群算法(Chaos Particle Swarm Optimization, CPSO)优化BP神经网络的感应电机转子断条故障诊断方法。该方法首先通过定子电流Hilbert模量进行故障特征提取,然后采用CPSO-BP神经网络进行故障状态的自动识别。Hilbert模量可以将定子电流中的基波信号转化为直流分量,降低其对特征提取的干扰,从而凸显故障特征。而CPSO-BP神经网络方法相比BP神经网络具有更好的权值系数,可以进一步提高故障识别率。经实例验证,基于Hilbert模量与改进BP神经网络的电机故障诊断方法性能良好。

英文摘要:

To identify 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 have superior initial weights and can strengthen the classification correctness. As a result, the experiment reminds the effectiveness and superiority of the proposed method.

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