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基于多块KPCA和SDG的故障诊断方法

, PP. 1473-1478

Keywords: 符号有向图,多块核主元分析,过程监控,故障定位

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Abstract:

针对大规模复杂工业过程,提出一种基于多块核主元分析(MBKPCA)和符号有向图(SDG)的故障诊断方法.首先,提出基于SDG和优先级的分块策略,以强连接元SCC为最高优先级、多入/出度节点群为次高优先级、节点链为最低优先级对过程进行分块;在此基础上,采用MBKPCA进行过程监控,对于检测到的故障,先确定故障发生在哪一个数据块,再触发SDG在故障块内完成故障定位.所提出方法克服了多块KPCA故障隔离不完全和SDG推理过程中组合爆炸的缺点,可以提高复杂工业过程故障诊断的准确度和速度.基于TennesseeEastman过程的仿真研究表明了所提出故障诊断方法的有效性.

References

[1]  Venkatasubramanian V, Rengaswamy R, Yin K. A
[2]  review of process fault detection and diagnosis, Part I:
[3]  Quantitative model-based methods[J]. Computers and
[4]  Chemical Engineering, 2003, 27: 293-311.
[5]  Zhao C H, Wang F L, Lu N Y. Stage-based soft-transition
[6]  batch processes[J]. J of Process Control, 2007, 17(9): 728-
[7]  741.
[8]  Aguado D, Rosen C. Multivariate statistical monitoring
[9]  of continuous wastewater treatment plants[J]. Engineering
[10]  Application of Artificial Intelligence, 2008, 21(7): 1080-
[11]  Computers and Chemical Engineering, 1996, 20: 65-78.
[12]  analysis with application to process fault detection[J]. Int J
[13]  of Systems Science, 2001, 31: 1473-1487.
[14]  Cho J, Lee J. Fault identification for process monitoring
[15]  using kernel principal component analysis[J]. Chemical
[16]  Engineering Science, 2005, 60: 279-288.
[17]  Sch¨olkopf B, Smola A, Müller K. Nonlinear component
[18]  nonlinear processes based on kernel PCA[J].
[19]  75(1): 55-67.
[20]  KPCA and SVM for real-time fault diagnosis of HVCBs[J].
[21]  IEEE Trans on Power Delivery, 2011, 26(3): 1960-1971. MacGregor J, Jackle C. Process monitoring and diagnosis
[22]  of large-scale processes using multiblock kernel principal
[23]  component analysis[J]. Acta Automatic Sinica, 2010,
[24]  Engineering Practice, 1999, 7: 903-917.
[25]  孙运莲. 基于分块和核参数选择的KPCA 研究[D]. 哈尔
[26]  滨: 哈尔滨工业大学计算机科学与技术学院, 2010.
[27]  (Sun Y L. Research on KPCA based on block and kernel
[28]  Science and Technology,Harbin Institute of Technology,
[29]  algorithm[J]. SIAM J on Computing, 1972, 1(2): 146-160.
[30]  Downs D, Vogel E. A plant-wide industrial process control
[31]  problem[J]. Computers&Chemical Engineering, 1993, 17:
[32]  245-255.
[33]  (Bie L B. Fault detection and diagnosis of continuous
[34]  process based on data-driven method[D]. Shenyang:
[35]  School of Information Science and Engineering, Shenyang
[36]  University of Technology, 2009.)
[37]  multiple PCA modeling and on-line monitoring strategy for
[38]  1091.
[39]  Dong D, McAvoy T. Nonlinear principal component
[40]  analysis based on principal curves and neural networks[J].
[41]  Jia F, Martin E, Morris A. Nonlinear principal components
[42]  analysis as a kernel eigenvalue problem[J]. Neural
[43]  Computation, 1998, 10(5): 1299-1319.
[44]  Choi S, Lee C, Lee J. Fault detection and identification of
[45]  Chemometrics and Intelligent Laboratory Systems, 2005,
[46]  Ni J, Zhang C, Yang S. An adaptive approach based on
[47]  by multiblock PLS methods[J]. AIChE Journal, 1994,
[48]  40(5): 826-838.
[49]  Zhang Y, Zhou H, Qin S. Decentralized fault diagnosis
[50]  36(4): 593-597.
[51]  Iri M, Aoki K, Shima E, et al. An algorithm for diagnosis of
[52]  system failures in the chemical process[J]. Computers and
[53]  Chemical Engineering, 1979, 3(1/4): 489-493.
[54]  Vedam H, Venkatasubramanian V. PCA-SDG based
[55]  process monitoring and fault diagnosis[J]. Control
[56]  parameter selection[D]. Harbin: School of Computer
[57]  2010.)
[58]  Tarjan R E. Depth-first search and linear graph
[59]  别立波. 基于数据驱动的连续过程故障发现与诊断研
[60]  究[D]. 沈阳: 沈阳工业大学信息科学与工程学院, 2009.

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