A Two-Step Fault Detection and Diagnosis Framework for Chemical Processes

https://doi.org/10.22146/ajche.50083

Lau Chee Kong(1*), Che Rosmani(2), Che Hasan(3), Mohd Azlan Huzzain(4)

(1) Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
(2) Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
(3) Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
(4) Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
(*) Corresponding Author

Abstract


An effective process monitoring system serves as an early warning system for influences affecting the chemical plant and helps plant operator to devise remedial actions to mitigate the adverse effects. However, the design of such system presents challenges such as complex cause-effect correlations, imprecise process model and novelty identifiability. In this work, a two-step fault detection and diagnosis framework is presented. This framework utilizes boundary models developed from mass and energy balances for each section of the chemical plant. The fault detection step consists of a fuzzy inference system (FIS) to analyze the balances and identify the faulty section if the balances deviate from the normal boundary. Then, multiple adaptive neuro-fuzzy inference system (ANFIS) classifiers are constructed to diagnose the exact root causes of bad performance. The combination of boundary models and FIS provides fault isolation of the faulty plant section even when novel faults have occurred. Utilization of multiple ANFIS classifiers reduces the complexity of the networks and improves the proficiency of the process monitoring system. The proposed scheme is applied on a model of a large scale industrial process.

Keywords


Fault detection, Fault diagnosis, Boundary models, FIS, ANFIS.

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References

  1. M. A. Hussain, Review of the applications of neural networks in process control – simulation and online implementation, J Artif. Intell. Eng. 13(1) (1999) 55-68.
  2. M. A. Hussain, C. R. Che Hassan, K. S. Loh and K. W. Mah: Application of artificial intelligence technique in process fault diagnosis, J. Eng. Sci. and Tech. 2(3) (2007) 260-270.
  3. C. K. Lau, Y. S. Heng, M. A. Hussain and M. I. Mohamad Nor: Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS, ISA T. (2010) doi:10.1016/j.isatra.2010.06/007.
  4. S. H. Jamal, M. A. Hussain, M. K. Aroua and D. Yaakop: Artificial neural network models applied to chemical engineering: a review, Trends Chem. Eng. 10 (2006) 1-15.
  5. J. Y. Fan, M. Nikolaou and R. E. White: An approach to fault diagnosis of chemical process via neural networks, AIChE J. 39 (1993) 82-88.
  6. Y. C. Kok, C. P. Lim and K. L. Weng: Fault diagnosis in a power generation plant using a neural fuzzy system with rule extraction. In: V. Palade, C.C. Bocanialam and L. Jain (Eds): Computational Intelligence in Fault Diagnosis (2001) Springer.
  7. K. Watanabe, I. Matsuura, M. Abe, M. Kubota and D. M. Himmelblau: Incipient fault diagnosis of chemical processes via artificial neural networks, AIChE J. 35(11) (1989) 1803-1989.
  8. Y. Power, P. A. Bahri: A two-step supervisory fault diagnosis framework, Comput. Chem. Eng. 28 (2004) 2131-2140.
  9. K. Watanabe, S. Hirota, L. Hou and D. M. Himmelblau: Diagnosis of multiple simulataneous faults via hierarchical artificial neural networks, AIChE J. 40(5) (1994) 839-848.
  10. B. Ozyurt and A. Kandel: A hybrid hierarchical neural network-fuzzy expert system approach to chemical process fault diagnosis, Fuzzy Sets Syst. 83 (1996) 11-25.
  11. R. Eslamloueyan: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process, Appl. Soft Comput. J. (2010), doi:10.1016/ j.asoc.2010.04.012.
  12. J.-S R. Jang and C.-T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (1997), Prentice Hall.
  13. B. R. Bakshi: Multiscale PCA with application to multivariate statistical process monitoring, AIChE J. 44(7) (1998) 1596-1610.
  14. J. J. Downs and E. F. Vogel: A plant-wide industrial process control problem, Comput. Chem. Eng. 17 (1993) 245-255.
  15. L. H. Chiang, E. L. Russell and R. D. Braatz: Fault Detection and Diagnosis in Industrial Systems (2006) Springer.



DOI: https://doi.org/10.22146/ajche.50083

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ASEAN Journal of Chemical Engineering  (print ISSN 1655-4418; online ISSN 2655-5409) is published by Chemical Engineering Department, Faculty of Engineering, Universitas Gadjah Mada.