2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于贡献率法的非线性工业过程在线故障诊断

彭开香 张凯 李钢

彭开香, 张凯, 李钢. 基于贡献率法的非线性工业过程在线故障诊断. 自动化学报, 2014, 40(3): 423-430. doi: 10.3724/SP.J.1004.2014.00423
引用本文: 彭开香, 张凯, 李钢. 基于贡献率法的非线性工业过程在线故障诊断. 自动化学报, 2014, 40(3): 423-430. doi: 10.3724/SP.J.1004.2014.00423
PENG Kai-Xiang, ZHANG Kai, LI Gang. Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes. ACTA AUTOMATICA SINICA, 2014, 40(3): 423-430. doi: 10.3724/SP.J.1004.2014.00423
Citation: PENG Kai-Xiang, ZHANG Kai, LI Gang. Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes. ACTA AUTOMATICA SINICA, 2014, 40(3): 423-430. doi: 10.3724/SP.J.1004.2014.00423

基于贡献率法的非线性工业过程在线故障诊断

doi: 10.3724/SP.J.1004.2014.00423
基金项目: 

Supported by National Natural Science Foundation of China (61074085), Beijing Natural Science Foundation (4122029, 4142035), and the Fundamental Research Funds for the Central Universities (FRF-SD-12-008B, FRF-AS-11-004B)

详细信息
    通讯作者:

    张凯

Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes

Funds: 

Supported by National Natural Science Foundation of China (61074085), Beijing Natural Science Foundation (4122029, 4142035), and the Fundamental Research Funds for the Central Universities (FRF-SD-12-008B, FRF-AS-11-004B)

  • 摘要: 在过去几十年,核主成分分析(KPCA)已经广泛应用在数据驱动的过程监测领域. 大量的应用案例显示该算法简单、易用且有效. 然而,核函数的引入使得KPCA不能直接利用传统的贡献图方法进行故障诊断. 本文在重新审视和分析现有KPCA相关诊断方法的基础上,提出了一类新的贡献率方法,该方法能较清晰地解释故障变量. 在此基础上,建立了一套面向非线性在线故障诊断的框架. 最后,将该诊断框架应用到CSTR过程,结果显示该方法较传统的线性方法更有效.
  • [1] Qin S J. Statistical process monitoring: basics and beyond. Journal of Chemometrics, 2003, 17(8-9): 480-512
    [2] [2] Yin S, Ding S X, Abandan Sari H A, Hao H Y, Zhang P Y. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 2012, 22(9): 1567-1581
    [3] [3] Yin S, Ding S X, Haghani A, Hao H Y. Data-driven monitoring for stochastic systems and its application on batch process. International Journal of Systems Science, 2013, 44(7): 1366-1376
    [4] [4] Liu J L. Fault diagnosis using contribution plots without smearing effect on non-faulty variables. Journal of Process Control, 2012, 22(9): 1609-1623
    [5] [5] Zhou Dong-Hua, Wei Mu-Heng, Si Xiao-Sheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes. Acta Automatica Sinica, 2013, 39(6): 711-722 (in Chinese)
    [6] [6] Qin S J, Zheng Y Y. Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures. AIChE Journal, 2013, 59(2): 496-504
    [7] [7] Cho J H, Lee J M, Choi S W, Lee D K, Lee I B. Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science, 2005, 60(1): 279-288
    [8] [8] Choi S W, Morris J, Lee I B. Nonlinear multiscale modelling for fault detection and identification. Chemical Engineering Science, 2008, 63(8): 2252-2266
    [9] [9] Rakotomamonjy A. Variable selection using SVM-based criteria. Journal of Machine Learning Research, 2003, 3: 1357-1370
    [10] Choi S W, Lee C Y, Lee J M, Park J H, Lee I B. Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems, 2005, 75(1): 55-67
    [11] Alcala C F, Qin S J. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Industrial Engineering Chemistry Research, 2010, 49(17): 7849-7857
    [12] Cremers D, Kohlberger T, Schnrr C. Shape statistics in kernel space for variational image segmentation. Pattern Recognition, 2003, 36(9): 1929-1943
    [13] Ding M T, Tian Z, Xu H X. Adaptive kernel principal component analysis. Signal Processing, 2010, 90(5): 1542-1553
    [14] Nguyen V H, Golinval J C. Fault detection based on kernel principal component analysis. Engineering Structures, 2010, 32(11): 3683-3691
    [15] Choi S W, Lee I B. Nonlinear dynamic process monitoring based on dynamic kernel PCA. Chemical Engineering Science, 2004, 59(24): 5897-5908
    [16] Liu X Q, Kruger U, Littler T, Xie L, Wang S Q. Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometrics and Intelligent Laboratory Systems, 2009, 96(2): 132-143
    [17] Zhang Y W, Li S, Hu Z Y, Song C H. Dynamical process monitoring using dynamical hierarchical kernel partial least squares. Chemometrics and Intelligent Laboratory Systems, 2012, 118: 150-158
    [18] Alcala C F, Qin S J. Reconstruction-based contribution for process monitoring. Automatica, 2009, 45(7): 1593-1600
    [19] Li G, Qin S J, Ji Y D, Zhou D H. Reconstruction based fault prognosis for continuous processes. Control Engineering Practice, 2010, 18(10): 1211-1219
    [20] Mika S, Schlkopf B, Smola A, Mller K R, Scholz M, Rtsch G. Kernel PCA and de-noising in feature spaces. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. Cambridge, MA, USA: MIT Press, 1999. 536-542
    [21] Schlkopf B, Mika S, Burges C J C, Knirsch P, Muller K, Ratsch G, Smola A J. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10(5): 1000-1017
    [22] Mler K R, Mika S, Rsch G, Tsuda K, Schkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
    [23] Peng K X, Zhang K, Li G, Zhou D H. Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process. Control Engineering Practice, 2013, 21(4): 360-369
    [24] Zhang Y W, Zhou H, Qin S J. Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis. Acta Automatica Sinica, 2010, 36(4): 593-597
    [25] Baffi G, Martin E B, Morris A J. Non-linear projection to latent structures revisited: the quadratic PLS algorithm. Computers and Chemical Engineering, 1999, 23(3): 395-411
    [26] Kruger U, Wang X, Chen Q, Qin S J. An alternative PLS algorithm for the monitoring of industrial process. In: Proceedings of the 2001 American Control Conference. Arlington, VA, USA: IEEE, 2001. 4455-4459
    [27] Li G, Qin S Z, Ji Y D, Zhou D H. Total PLS based contribution plots for fault diagnosis. Acta Automatica Sinica, 2009, 35(6): 759-765
  • 加载中
计量
  • 文章访问数:  2277
  • HTML全文浏览量:  114
  • PDF下载量:  1148
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-05-29
  • 修回日期:  2013-08-23
  • 刊出日期:  2014-03-20

目录

    /

    返回文章
    返回