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An artificial immune system for adaptive fault detection, diagnosis and recovery

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Abstract

The human immune system provides rich metaphors for adaptive pattern recognition. Fault detection and diagnosis in chemical processes is commonly formulated as a pattern recognition problem. However, conventional methods for fault diagnosis often do not have a mechanism to adapt and learn as the process changes over time. In this paper, we propose an Artificial Immune System (AIS) framework that endows learning to statistical process monitoring techniques such as Principal component analysis. The proposed AIS framework also provides a direct means to incorporate recovery actions after a failure has been detected and diagnosed. We demonstrate the efficacy of the proposed framework using a simulated binary distillation column case study.

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References

  1. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N.: A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies. Comput. Chem. Eng. 27(313–326), 2003 (2002)

    Google Scholar 

  2. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.: A review of process fault detection and diagnosis. Part III: Process history based methods. Comput. Chem. Eng. 27(293–311), 2003 (2002)

    Google Scholar 

  3. Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis. Part I: Quantative model-based methods. Comput. Chem. Eng. 27(293–311), 2003 (2002)

    Google Scholar 

  4. de Castro, L.N., Timmis, J.: Artificial immune systems: a new computational intelligence approach. Springer, New York (2002)

    MATH  Google Scholar 

  5. Powers, S.T., He, J.: A hybrid artificial immune system and Self Organising Map for network intrusion detection. Inf. Sci. 178, 3024–3042 (2008)

    Article  Google Scholar 

  6. Paquete, L., Stützle, T.: Design and analysis of stochastic local search for the multiobjective traveling salesman problem. Comput. Oper. Res. 36, 2619–2631 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Guimarães, F.G., Palhares, R.M., Campelo, F., Igarashi, H.: Design of mixed H2/H∞ control systems using algorithms inspired by the immune system. Inf. Sci. 177, 4368–4386 (2007)

    Article  MATH  Google Scholar 

  8. Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Appl. Soft Comput. J. 11, 1574–1587 (2011)

    Article  Google Scholar 

  9. Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. Evol. Comput. 11(4), 521–540 (2007)

    Article  Google Scholar 

  10. Laurentys, C.A., Ronacher, G., Palhares, R.M., Caminhas, W.M.: Design of an artificial immune system for fault detection: a negative selection approach. Expert Syst. Appl. 37, 5507–5513 (2010)

    Article  Google Scholar 

  11. Ghosh, K., Srinivasan, R.: Immune-system-inspired approach to process monitoring and fault diagnosis. Ind. Eng. Chem. Res. 50, 1637–1651 (2011)

    Article  Google Scholar 

  12. Cho, J.H., Lee, J.M., Choi, S.W., Lee, D., Lee, I.B.: Fault identification for process monitoring using kernel principal component analysis. Chem. Eng. Sci. 60, 279–288 (2005)

    Article  Google Scholar 

  13. Srinivasan, R., Wang, C., Ho, W.K., Lim, K.W.: Dynamic principal component analysis based methodology for clustering process states in agile chemical plants. Ind. Eng. Chem. Res. 43, 2123–2139 (2004)

    Article  Google Scholar 

  14. Qin, J.S.: Statistical process monitoring: basics and beyond. J. Chemom. 2003(17), 480–502 (2003)

    Article  Google Scholar 

  15. Srinivasan, R., Viswanathan, P., Vedam, H., Nochur, A.: A framework for managing transitions in chemical plants. Comput. Chem. Eng. 29, 305–322 (2005)

    Article  Google Scholar 

  16. Krzanowski, W.J.: Between-groups comparison of principal components. J Am Stat Assoc 74(367), 703–707 (1979)

    MathSciNet  MATH  Google Scholar 

  17. Diehl, M.: Real-time optimization for large scale nonlinear processes. PhD thesis, University of Heidelberg (2001)

  18. de Castro, L.N., Von Zuben, F.J.: An evolutionary immune network for data clustering. In: Proceedings of the IEEE Brazilian Symposium on Artificial Neural Networks, pp. 84–89 (2000a)

  19. de Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Proccedings of GECCO’00, pp. 36–37 (2000b)

  20. de Castro, L.N., Timmis, J.I.: Artificial Immune Systems: a novel paradigm to pattern recognition. In: Corchado, J.M., Alonso, L., and Fyfe C. (eds.) Artificial neural networks in pattern recognition, pp. 67–84. SOCO-2002, University of Paisley, Paisley (2002)

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Acknowledgments

The authors express their gratitude to Mr. Sathish Natarajan for assistance in the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Chemical and Biomolecular Engg, National University of Singapore, 10 Kent Ridge Crescent, Singapore, 119260, Singapore

    Chee Chun Kiang & Rajagopalan Srinivasan

  2. Process Sciences and Modeling, Institue of Chemical and Engineering Science, 1 Pesek Road, Jurong Island, 627833, Singapore

    Rajagopalan Srinivasan

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  1. Chee Chun Kiang
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  2. Rajagopalan Srinivasan
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Correspondence to Rajagopalan Srinivasan.

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Kiang, C.C., Srinivasan, R. An artificial immune system for adaptive fault detection, diagnosis and recovery. Int J Adv Eng Sci Appl Math 4, 22–31 (2012). https://doi.org/10.1007/s12572-012-0054-2

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