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一种基于非线性独立元分析(NICA)的化工过程监控方法 被引量：1

Industrial Process Monitoring and Fault Diagnosis with Nonlinear Independent Component Analysis
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摘要 针对实际工业过程数据中的非线性问题,研究了一种基于非线性独立元分析的多变量过程监控方法。该方法根据贝叶斯原理,构造多层感知器网络恢复过程数据,并以此建立过程的数学统计模型,对其进行实时监控。在大型工业设备仿真器TE上的应用表明了该方法的有效性,同时,在故障诊断方面也体现出了一定的优越性。 Considering the nonlinear characteristic of date in real industry processes, a multivariable process monitoring method based on nonlinear independent component analysis （NICA） is presented. With the help of Bayesian theorem, process data can be reconstructed by establishing multi-layer perceptrons, and statistical model of process in mathematics can be given for monitoring in real time. The proposed method is applied to the Tennessee- Eastman （TE） process. Simulation results show its availability and advantage in the aspect of fault diagnosis.
作者 刘飞 吴昌应
机构地区 江南大学自动化研究所
出处 《华东理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2006年第7期806-809,共4页 Journal of East China University of Science and Technology
基金 新世纪优秀人才支持计划
关键词 非线性独立元分析 多层感知器 过程监控 故障诊断 TE过程 NICA multi-layer perceptions process monitoring fault diagnosis TE process
分类号 TP206.3 [自动化与计算机技术—检测技术与自动化装置]
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参考文献10

  • 1Chiang L H,Russell E L,Braatz R D.Fault Detection and Diagnosis in Industrial System[M].London:Springer-Verlag,2001.
  • 2Hyvarinen A.Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Transaction on Neural Network,1999,10(3):626-634.
  • 3Pierre C.Independent component analysis:A new concept?[J].Signal Processing,1994,36:287-314.
  • 4Huang X M,Liu C M,Zhang L M.Study on object recognition based on independent component analysis[A].Advances in Neural Networks-ISNN 2004[C].Dalian:Springer-Verlag,2004.720-725.
  • 5Lee J M,Yoo C K,Lee I B.Statistical process monitoring with independent component analysis[J].Journal of Process Control,2004,14:467-485.
  • 6陈国金,梁军,钱积新.独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用[J].化工学报,2003,54(10):1474-1477. 被引量：30
  • 7Honkela L H,Bayesian nonlinear independent component analysis by multi-layer perceptrons[A].Advanced in Independent Component Analysis[C].Berlin:Springer,2000.93-121.
  • 8Hyvarinen A,Pajunen P.Nonlinear independent component analysis:Existence and uniqueness results[J].Neural Network,1999,12:209-219.
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  • 10Downs J J,Vogel E F.A plant-wide industrial process control problem[J].Computers Chemical Engineering,1993,17(3):245-255.

二级参考文献6

  • 1梁军,钱积新. Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry. Chinese J of Chemical Eng, 2003, 11 (2): 191--203.
  • 2Johnson R A, Wichern D W. Applied Multivariate Statistical Analysis. 4th ed. Englewood Cliff, NJ: Prentice Hall, 1998.
  • 3Ypma A, Tax D, Duin R. Robust Machine Fault Detection with Independent Component Analysis and Support Vector Data Description. In: Proc of the 1999 IEEE Signal Processing Society Workshop, 1999.67 - 76.
  • 4Hyvarinen A, Oia E. Independent Component Analysis:Algorithms and Applications. Neural Networks, 2002, 13:411-430.
  • 5Tax D, Duin R. Support Vector Domain Description. Pattern Recognition Letters , 1999, 20:1191 -- 1199.
  • 6Luyben W. Process Modeling, Simulation, and Control for Chemical Engineers. 2nd ed. New York: McGraw-Hill, 1988.

共引文献29

同被引文献6

  • 1Pierre C. Independent component analysis: A new concept? [J]. Signal Processing. 1994. 36(3): 287-314.
  • 2Hyvarinen A. Independent Component Analysis [M]. New York: John Wiley and Sons, 2001.
  • 3Luis B Almeida.Linear and nonlinear ICA based on mutual information-the MISEP method[J]. Signal Processing.2004.84 (2):231-245.
  • 4Zheng Chun-Hou, Huang De-Shuang,Li Kang,et al. MISEP method for postnonlinear blind source separation[J]. Neural Computation. 2007,19(9): 2557-2578.
  • 5Lee Jong-Min,Yoo ChangKyoo,Lee In Beum. Statistical monitoring of dynamic processes based on dynamic independent component analysis[J]. Chemical Engineering Science, 2004, 59( 14 ) : 2995-3006.
  • 6Downs J J, Vogel EF. A plant wide industrial process control problem[J]. Computers Chemical Engineering, 1993, 17(3):245-255.

引证文献1

二级引证文献2

华东理工大学学报（自然科学版）
2006年 第7期

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