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Fault detection based on polygon area statistics of transformation matrix identified from combined moving window data

  • Process Systems Engineering, Process Safety
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Abstract

Principal component analysis (PCA) has been widely used in monitoring industrial processes, but it is still necessary to make improvements in having a timely and effective access to variation information. It is known that the transformation matrix generated from real-time PCA model indicates inner relations between original variables and new produced components, so this matrix would be different when modeling data deviate due to the change of the operating condition. Based on this theory, this paper proposes a novel real-time monitoring approach which utilizes polygon area method to measure the variation degree of the transformation matrices and then constructs a statistic for monitoring purpose. The on-line data are collected through a combined moving window (CMW), containing both normal and monitored data. To evaluate the performance of the proposed method, a simple numerical simulation, the CSTR process and the classic Tennessee Eastman process are employed for illustration, with some PCA-based methods used for comparison.

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References

  1. J.-M. Lee, C. Yoo and I.-B. Lee, J. Process Control, 14, 467 (2004).

    Article  CAS  Google Scholar 

  2. A. Nowicki, M. Grochowski and K. Duzinkiewicz, International J. Appl. Mathematics Computer Sci., 22, 939 (2012).

    Article  Google Scholar 

  3. Q. Jiang, X. Yan, Z. Lv and M. Guo, Korean J. Chem. Eng., 30, 1181 (2013).

    Article  CAS  Google Scholar 

  4. S. S. Kolluri, I. J. Esfahani, P. S. N. Garikiparthy and C. Yoo, Korean J. Chem. Eng., 32(8), 1486 (2015).

    Article  CAS  Google Scholar 

  5. C. Lu, S. Xiao and X. Gu, Korean J. Chem. Eng., 31, 1943 (2014).

    Article  CAS  Google Scholar 

  6. I. Jolliffe, Principal component analysis, Wiley Online Library (2005).

    Book  Google Scholar 

  7. R. Wehrens, in Principal component analysis, pp. 43–66, Springer, (2011).

    Google Scholar 

  8. L. H. Chiang, R. D. Braatz and E. L. Russell, Fault detection and diagnosis in industrial systems, Springer Science & Business Media (2001).

    Book  Google Scholar 

  9. B. Schölkopf, A. Smola and K.-R. Müller, Neural Computation, 10, 1299 (1998).

    Article  Google Scholar 

  10. J.-D. Shao, G. Rong and J. M. Lee, Chem. Eng. Res. Design, 87, 1471 (2009).

    Article  CAS  Google Scholar 

  11. C.-Y. Cheng, C.-C. Hsu and M.-C. Chen, Ind. Eng. Chem. Res., 49, 2254 (2010).

    Article  CAS  Google Scholar 

  12. Q. Jiang and X. Yan, Korean J. Chem. Eng., 31, 1935 (2014).

    Article  CAS  Google Scholar 

  13. A. Hyvärinen and E. Oja, Neural Networks, 13, 411 (2000).

    Article  Google Scholar 

  14. A. Hyvärinen, J. Karhunen and E. Oja, Independent component analysis, Wiley (2004).

    Google Scholar 

  15. Z. Ge and Z. Song, Korean J. Chem. Eng., 26, 1467 (2009).

    Article  CAS  Google Scholar 

  16. Z. Ge and Z. Song, Ind. Eng. Chem. Res., 46, 2054 (2007).

    Article  CAS  Google Scholar 

  17. C. Tong, Y. Song and X. Yan, Ind. Eng. Chem. Res., 52, 9897 (2013).

    Article  CAS  Google Scholar 

  18. Z. Ge, M. Zhang and Z. Song, J. Process Control, 20, 676 (2010).

    Article  CAS  Google Scholar 

  19. B. Wang, X. Yan, Q. Jiang and Z. Lv, J. Chemometrics, 29(3), 165 (2014).

    Article  Google Scholar 

  20. P. Nomikos and J. F. MacGregor, AIChE J., 40, 1361 (1994).

    Article  CAS  Google Scholar 

  21. N. A. A. Majid, M. P. Taylor, J. J. Chen, M. A. Stam, A. Mulder and B. R. Young, Control Eng. Practice, 19, 367 (2011).

    Article  Google Scholar 

  22. W. Li, H. H. Yue, S. Valle-Cervantes and S. J. Qin, J. Process Control, 10, 471 (2000).

    Article  CAS  Google Scholar 

  23. C. Cheng and M.-S. Chiu, Chemometrics and Intelligent Laboratory Systems, 76, 1 (2005).

    Article  CAS  Google Scholar 

  24. T. Korenius, J. Laurikkala and M. Juhola, Information Sciences, 177, 4893 (2007).

    Article  Google Scholar 

  25. C. Lau, K. Ghosh, M. Hussain and C. Che Hassan, Chemometrics and Intelligent Laboratory Systems, 120, 1 (2013).

    Article  CAS  Google Scholar 

  26. L. C. Alwan and H. V. Roberts, J. Business Economic Statistics, 6, 87 (1988).

    Google Scholar 

  27. D. Montgomery, C. Mastrangelo, F. W. Faltin, W. H. Woodall, J. F. MacGregor and T. P. Ryan, J. Quality Technol., 23, 179 (1991).

    Google Scholar 

  28. S. Vander Wiel, Technometrics, 38, 139 (1996).

    Article  Google Scholar 

  29. A. Negiz and A. Çlinar, AIChE J., 43, 2002 (1997).

    Article  CAS  Google Scholar 

  30. A. Simoglou, E. Martin and A. Morris, Computers Chem. Eng., 26, 909 (2002).

    Article  CAS  Google Scholar 

  31. W. Ku, R. H. Storer and C. Georgakis, Chemometrics and Intelligent Laboratory Systems, 30, 179 (1995).

    Article  CAS  Google Scholar 

  32. W. Jingyuan, S. Zhijiang, J. Peng, Y. Ketian and C. Zhiqiang, Computers Appl. Chem., 1, 2 (2010).

    Google Scholar 

  33. S. R. Ryu, I. Noda and Y. M. Jung, Bull. Korean Chem. Soc., 32, 2232 (2011).

    Google Scholar 

  34. X. Wang, U. Kruger and G. W. Irwin, Ind. Eng. Chem. Res., 44, 5691 (2005).

    Article  CAS  Google Scholar 

  35. E. R. Malinowski, Factor analysis in chemistry, Wiley (2002).

    Google Scholar 

  36. J. Camacho and A. Ferrer, J. Chemometrics, 26, 361 (2012).

    Article  CAS  Google Scholar 

  37. S. Valle, W. Li and S. J. Qin, Ind. Eng. Chem. Res., 38, 4389 (1999).

    Article  CAS  Google Scholar 

  38. Q. Jiang, X. Yan and W. Zhao, Ind. Eng. Chem. Res., 52, 1635 (2013).

    Article  CAS  Google Scholar 

  39. Q. Jiang and X. Yan, AIChE J., 60, 949 (2014).

  40. C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, Springer New York (2006).

    Google Scholar 

  41. M. Kano, S. Hasebe, I. Hashimoto and H. Ohno, AIChE J., 48, 1231 (2002).

    Article  CAS  Google Scholar 

  42. D. P. Robbins, Discrete Computational Geometry, 12, 223 (1994).

    Article  Google Scholar 

  43. I. Pak, Advances in Appl. Mathematics, 34, 690 (2005).

    Article  Google Scholar 

  44. S. Yoon and J. F. MacGregor, J. Process Control, 11, 387 (2001).

    Article  CAS  Google Scholar 

  45. J.-H. Cho, J.-M. Lee, S. W. Choi, D. Lee and I.-B. Lee, Chem. Eng. Sci., 60, 279 (2005).

    Article  CAS  Google Scholar 

  46. M. Mansouri, M. Nounou, H. Nounou and N. Karim, J. Loss Prevent. Proc. 40, 334 (2016).

  47. C. F. Alcala and S. J. Qin, Ind. Eng. Chem. Res., 49, 7849 (2010).

    Article  CAS  Google Scholar 

  48. J. J. Downs and E. F. Vogel, Comput. Chem. Eng., 17, 245 (1993).

    Article  CAS  Google Scholar 

  49. Q. Jiang, X. Yan and B. Huang, IEEE T. Ind. Electron., 63, 377 (2015).

    Article  Google Scholar 

  50. K.-L. Du and M. Swamy, Neural Networks and Statistical Learning, 355, Springer (2014).

    Book  Google Scholar 

  51. B. Wang, Q. Jiang and X. Yan, Korean J. Chem. Eng., 31, 930 (2014).

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, P. R. China

    Bei Wang, Xuefeng Yan & Yongfei Jin

Authors
  1. Bei Wang
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  2. Xuefeng Yan
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  3. Yongfei Jin
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Correspondence to Xuefeng Yan.

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Wang, B., Yan, X. & Jin, Y. Fault detection based on polygon area statistics of transformation matrix identified from combined moving window data. Korean J. Chem. Eng. 34, 275–286 (2017). https://doi.org/10.1007/s11814-016-0201-8

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  • DOI: https://doi.org/10.1007/s11814-016-0201-8

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