辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2010, Vol. 30 ›› Issue (3): 79-81.DOI: 10.3696/j.issn.1672-6952.2010.03.022

• 计算机与自动化 • 上一篇    下一篇

基于小波神经网络的化学反应器故障模式识别

刘晓琴   

  1. 辽宁石油化工大学信息与控制工程学院,辽宁抚顺113001
  • 收稿日期:2009-05-16 出版日期:2010-09-25 发布日期:2017-07-05
  • 作者简介:刘晓琴(1975-),女,辽宁辽阳县,副教授,在读博士。

Fault Pattern Recognition of Chemical Reactor Based on Wavelet Neural Network

LIU Xiao-qin   

  1. School of Information and Control Engineering，Liaoning Shihua University, Fushun Liaoning 113001, P.R.China
  • Received:2009-05-16 Published:2010-09-25 Online:2017-07-05

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摘要： 利用具有BP算法的前馈神经网络(MFNN),针对反应器建立了过程数据与故障类型之间的对应关
系,辨识出系统的正常运行状态与故障运行状态。为了提高辨识的准确度,利用小波技术改进MFNN 的作用函数
构成了小波神经网络(WNN)。对化学反应器中的一类典型反应过程进行了仿真实验,实验结果表明,WNN 的故障
辨识比MFNN的故障类型辨识具有更高的准确率。

关键词: 故障 , 辨识 , 小波神经网络

Abstract:  

Using the multilayer forward neural networks(MFNN) based on back propagation(BP) algorithm, the relationship between the process measurements and fault type was constructed, the identification of the normal state and fault state was achieved. For improving the accuracy of identification, wavelet technology was used, the activation function of MFNN was modified and wavelet neural network (WNN) was constructed. The simulation results of a classical reaction process of chemical reactor show that WNN has higher accuracy than MFNN for fault identification.

Key words: Fault ,  Identification ,  Wavelet neural network

引用本文

刘晓琴. 基于小波神经网络的化学反应器故障模式识别[J]. 辽宁石油化工大学学报, 2010, 30(3): 79-81.

LIU Xiao-qin. Fault Pattern Recognition of Chemical Reactor Based on Wavelet Neural Network[J]. Journal of Liaoning Petrochemical University, 2010, 30(3): 79-81.

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