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神经网络的故障诊断方法研究

DOI:10.16185/j.j xatu.edu.cn.2015.07.003

神经网络的故障诊断方法研究倡

耿朝阳,薛倩倩

(西安工业大学计算机科学与工程学院,西安710021)

摘 要: 为了解决智能诊断应用中BP神经网络收敛速度慢、稳定性差以及精度不高的问

题,通过嵌入到设备中的诊断Agent采集到设备各元件工作电压,以此为对象研究基于Elman

的神经网络故障诊断方法,使用设备故障信息作为BP神经网络和Elman神经网络的训练样

本.结果表明,在相同的神经网络训练样本和测试样本下,BP神经网络的收敛速度比Elman

神经网络慢,Elman神经网络比BP神经网络诊断精度有提高.经过对训练过程和仿真结果的

分析,验证了基于Elman神经网络的故障诊断方法收敛速度提高了约2倍、精确度提高约1.5

倍,满足系统在线故障诊断需求.

关键词: 故障诊断;BP神经网络;Elman神经网络;Agent

中图号: T P301.6 文献标志码: A文章编号: 1673‐9965(2015)07‐0527‐07

Research on Fault Diagnosis Method Based on Neural Network

GENG Chao桘y an g,X UE Qian桘q ian

(School of Computer Science and Engineering,Xi’an Technological University,Xi’an710021,China)Abstract: Study aims to solve the problems of BP neural network’s low convergence speed,p oor stability and low accuracy in intelligent fault diagnosis.T hrough the diagnostic Agent embedded into the equipment,the working voltage across each component is collected.Based on the working voltages,the fault diagnosis method based on Elman neural network is studied.T he equipment fault information is used as the training samples for the BP neural network and Elman neural network.T he results show that with the same neural network training samples and testing samples,the convergence rate of BP neural network is lower than that of the Elman neural network.T he diagnostic accuracy of the Elman neural network is better than that of BP neural network.T he analysis of the training process and the simulation results show s that the convergence speed of the new method is about3times w hat it was and its accuracy is increased by about1.5times,meeting the needs of online fault diagnosis systems.

Key words: fault diagnosis;BP neural network;elman neural network;agent

随着现代化的发展,设备的规模和复杂度越来越高,对设备运行效率的要求越来越高,因此对设备在线故障检测和故障诊断方法有了更高的要求,故障诊断方法的研究越来越受到关注.由于设备故障信息的不确定性,以及故障现象与故障原因之间的复杂的非线性关系,故障诊断的难度越来越高,

第35卷第7期2015年7月

西 安 工 业 大 学 学 报

Journal of Xi’an Technological University

Vol.35No.7

Jul.2015

倡收稿日期:2015‐03‐16

基金资助:陕西省教育厅自然科学基金(2013JK1159)

作者简介:耿朝阳(1971‐),男,西安工业大学副教授,主要研究方向为人工智能、计算机仿真.E‐mail:g engbox@163.com.

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