Skip to main content
SpringerLink
Log in

MultiBoost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

  • Mechanical Engineering, Control Science and Information Engineering
  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Fault diagnosis plays an important role in complicated industrial process. It is a challenging task to detect, identify and locate faults quickly and accurately for large-scale process system. To solve the problem, a novel MultiBoost-based integrated ENN (extension neural network) fault diagnosis method is proposed. Fault data of complicated chemical process have some difficult-to-handle characteristics, such as high-dimension, non-linear and non-Gaussian distribution, so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features. Then, the affinity propagation (AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time. Afterwards, an integrated ENN classifier based on MultiBoost strategy is constructed to identify fault types. The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters. Finally, a real industrial system—Tennessee Eastman (TE) process is employed to evaluate the performance of the proposed method. And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. GAO Dong, WU Chong-guang, ZHANG Bei-ke, MA Xin. Signed directed graph and qualitative trend analysis based fault diagnosis in chemical industry [J]. Chinese Journal of Chemical Engineering, 2010, 18(2): 265–276.

    Article  Google Scholar 

  2. MAURYA M R, RENGASWAMY R. Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets [J]. Engineering Application of Artificial Intelligence, 2004, 17(5): 501–518.

    Article  Google Scholar 

  3. LAU C K, GHOSH K, HUSSAIN M A, CHE HASSAN C R. Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS [J]. Chemometrics and Intelligent Laboratory, 2013, 120: 1–14.

    Article  Google Scholar 

  4. YU Jie. A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data [J]. AIChE Journal, 2013, 59(2): 407–419.

    Article  Google Scholar 

  5. JIANG Qing-chao, YAN Xue-feng. Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring [J]. AIChE Journal, 2014, 60(3): 949–965.

    Article  Google Scholar 

  6. WANG Xian-fang, WANG Sui-hua, DU Hao-ze, WANG Ping. Fault diagnosis of chemical industry process based on FRS and SVM [J]. Control and Decision, 2015, 30(2): 353–356. (in Chinese)

    Google Scholar 

  7. LU Feng, LI Xiang, DU Wen-xia. MultiBoost with SVM-based ensemble classification method and application [J]. Control and Decision, 2015, 30(1): 81–85. (in Chinese)

    MATH  Google Scholar 

  8. CHEN Xin-yi, YAN Xue-feng. Fault diagnosis in chemical process based on self-organizing map integrated with fisher discriminant analysis [J]. Chemical Journal of Chemical Engineering, 2013, 21(4): 382–387.

    Google Scholar 

  9. SONG Bing, MA Yu-xin, FANG Yong-feng, SHI Hong-bo. Fault detection for chemical process based on LSNPE method [J]. Chinese Journal of Chemical Engineering Journal, 2014, 65(2): 620–627. (in Chinese)

    Google Scholar 

  10. ZHOU Yu, PEDRYCZ W, QIAN Xu. Application of extension neural network to safety status pattern recognition of coal mines [J]. Joumal of Central South University of Technology, 2011, 18: 633–641.

    Article  Google Scholar 

  11. BARAKAT M, DRUAUX F, LEFEBVRE D, KHALIL M, MUSTAPHA O. Self adaptive growing neural network classifier for faults detection and diagnosis [J]. Neurocomputing, 2011, 74: 3865–3876.

    Article  Google Scholar 

  12. ZHU Zhi-bo, SONG Zhi-huan. A novel fault diagnosis system using pattern classification on kernel FDA subspace [J]. Expert System with Application, 2011, 38(6): 6895–6905.

    Article  Google Scholar 

  13. JIANG Qing-chao, YAN Xue-feng. Probabilistic weighted NPE-SVDD for chemical process monitoring [J]. Control Engineering Practice, 2014, 28: 74–89.

    Article  Google Scholar 

  14. LI Xiao-qian, SONG Qiang, LIU Wen-hua, RAO Hong, XU Shu-kai, LI Li-cheng. Protection of nonpermanent faults on DC overhead lines in MMC-based HVDC systems [J]. IEEE Trans on Power Delivery, 2013, 28(1): 483–490.

    Article  Google Scholar 

  15. GAO Hui-hui, HE Yan-li, ZHU Qun-xiong. Research and chemical application of data attributes decomposition based hierarchical ELM neural network [J]. Chinese Journal of Chemical Engineering, 2013, 64(12): 4348–4353. (in Chinese)

    Google Scholar 

  16. HE Ding, ZHANG Jin-song. Fault strategy of time delay analysis based on Hopfield network [J]. Chinese Journal of Chemical Engineering, 2013, 64(2): 633–640. (in Chinese)

    Google Scholar 

  17. PENG Di, HE Yan-lin, XU Yan, ZHU Qun-xiong. Research and chemical application of data feature extraction based AANN-ELM neural network [J]. CIESC Journal, 2012, 63(9): 2920–2925. (in Chinese)

    Google Scholar 

  18. WANG Meng-hui. Partial discharge pattern recognition of current transformers using an ENN [J]. IEEE Trans on Power Delivery, 2005, 20(3): 1984–1990.

    Article  Google Scholar 

  19. WANG Meng-hui, HUNG C P, Extension neural network and its applications [J]. Neural Networks, 2003, 16(5): 779–784.

    Article  Google Scholar 

  20. CHAO K H, WANG M H, HSH C C. A novel residual capacity estimation method based on extension neural network for lead-acid batteries [J]. Lecture Notes in Computer Science, 2007, 4493: 1145–1154.

    Google Scholar 

  21. CHAO K H, LEE R H, WANG M H. An intelligent traffic light control based on extension neural network [J]. Lecture Notes in Computer Science, 2008, 5177: 17–24.

    Article  Google Scholar 

  22. TANG Sheng, ZHENG Yan-tao, WANG Yu, CHUA Tat-seng. Sparse ensemble learning for concept detection [J]. IEEE Trans on Multimedia, 2012, 14(1): 43–54.

    Article  Google Scholar 

  23. SHEN Chun-hua, HAO Zhi-hui. A direct formulation for totallycorrective multi-class boosting [C]// Computer Vision and Pattern Recognition (CVPR) of 2011 IEEE Conference. Washington D.C.: IEEE Computer Society, 2011: 2585–2592.

    Google Scholar 

  24. DJALEL B, ROBERT B F, NORMAN C, COLIN F D, BALAZS K. MultiBoost: A multi-purpose boosting package [J]. The Journal of Machine Learning Research, 2012, 13(1): 549–553.

    MATH  Google Scholar 

  25. HE Jin-rong, DING Li-xin, LI Zhao-kui, HU Qing-hui. Margin discriminant projection for dimensionality reduction [J]. Journal of Software, 2014, 25(4): 826–838. (in Chinese)

    Google Scholar 

  26. SHANG F H, JIAO L C, SHI J R, WANG F, GONG M G. Fast affinity propagation clustering: A multilevel approach [J]. Pattern Recognition, 2012, 45(1): 474–486.

    Article  Google Scholar 

  27. WANG Chang-dong, LAI Jian-huang, SUEN Ching Y, ZHU Jun-yong. Multi-exemplar affinity propagation [J]. Pattern Analysis and Machine, 2013, 35(9): 2223–2237.

    Article  Google Scholar 

  28. LIN Yuan-qing, LV Feng-jun, ZHU Sheng-huo, YANG Ming, COUR T, YU Kai, CAO Liang-liang, HUANG T. Large-scale image classification: Fast feature extraction and SVM training [C]// Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference. 2011: 1689–1696.

    Google Scholar 

  29. YAN Shui-cheng, XU Dong, ZHANG Ben-yu, ZHANG Hong-jiang, ZHANG Qiang, LIN S. Graph embedding and extensions: A general framework for dimensionality reduction [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40–51.

    Article  Google Scholar 

  30. SHAWE-TAYLOR J, CRISTIANINI N. Kernel methods for pattern analysis [M]. Cambriddgeshire: Cambridge University Press, 2004.

    Book  MATH  Google Scholar 

  31. GALAR M, FERNANDEZ A, BARRENECHEA E, BUSTINCE H, HERRER F. A review on ensembles for the class inbalance problem: Bagging, Boosting, and hybrid-based approach [J]. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2012, 42(4): 463–484.

    Article  Google Scholar 

  32. KROKROGH A, VEDELSBY J. Neural network ensembles,cross validation, and active learning [M]. Cambridge, MA: MIT Press, 1995: 231–238.

    Google Scholar 

  33. GUAN R C, SHI X H, MARCHESE M, YANG C, LIANG Y. Text clustering with seeds affinity propagation [J]. IEEE Transaction on Knowledge and Data Engineering, 2011, 23(4): 627–637.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Control Engineering, Liaoning Shihua University, Fushun, 113001, China

    Chong-kun Xia  (夏崇坤), Cheng-li Su  (苏成利), Jiang-tao Cao  (曹江涛) & Ping Li  (李平)

Authors
  1. Chong-kun Xia  (夏崇坤)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-li Su  (苏成利)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiang-tao Cao  (曹江涛)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping Li  (李平)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng-li Su  (苏成利).

Additional information

Foundation item: Project(61203021) supported by the National Natural Science Foundation of China; Project(2011216011) supported by the Key Science and Technology Program of Liaoning Province, China; Project(2013020024) supported by the Natural Science Foundation of Liaoning Province, China; Project(LJQ2015061) supported by the Program for Liaoning Excellent Talents in Universities, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Ck., Su, Cl., Cao, Jt. et al. MultiBoost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process. J. Cent. South Univ. 23, 1183–1197 (2016). https://doi.org/10.1007/s11771-016-0368-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-016-0368-5

Key words

Search

Navigation