Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 45 - Number 22
Year of Publication: 2012
Authors:
A. Khoukhi
H. Khalid
R. Doraiswami
L. Cheded
10.5120/7079-9312

A Khoukhi, H Khalid, R Doraiswami and L Cheded. Article: Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems. International Journal of Computer Applications 45(22):7-14, May 2012. Full text available. BibTeX

@article{key:article,
	author = {A. Khoukhi and H. Khalid and R. Doraiswami and L. Cheded},
	title = {Article: Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {45},
	number = {22},
	pages = {7-14},
	month = {May},
	note = {Full text available}
}

Abstract

In this paper, an efficient scheme to detect and classify faults in a system using kalman filtering and hybrid neuro-fuzzy computing techniques, respectively, is proposed. A fault is detected whenever the moving average of the Kalman filter residual exceeds a threshold value. The fault classification has been made effective by implementing a hybrid neuro-fuzzy Inference system. By doing so, the critical information about the presence or absence of a fault is gained in the shortest possible time, with not only confirmation of the findings but also an accurate unfolding-in-time of the finer details of the fault, thus completing the overall fault diagnosis picture of the system under test. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupled-tank system.

References

  • Simani, S. , Fantuzzi, C. , Patton, R. J. , 2003. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Springer, London.
  • Isermann, R. , 2005. Model-based fault-detection and diagnosis—status and applications. Annual Reviews in Control 29, 71–85.
  • Chow, E. Y. , Willsky, A. S. , 1984. Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control 29 (7), 603–614.
  • Gertler, J. J. , 1998. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York.
  • Frank, P. M. , Ko¨ ppen-Seliger, B. , 1997. New developments using AI in fault diagnosis. Engineering Applications of Artificial Intelligence 10 (1), 3–14.
  • Frank, P. M. , Ding, S. X. , Ko¨ ppen-Seliger, B. , 2000. Current developments in the theory of FDI. Proceedings of IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, vol. 1, Budapest, Hungary, pp. 16–27.
  • Haris M. Khalid, R. Doraiswami and L. Cheded, "Intelligent Fault Diagnosis using a Sensor Network", ICINCO, Milan, Italy, July 2-5, 2009.
  • R. Doraiswami, L. Cheded, H. M. Khalid, Q. Ahmed and A. Khoukhi, "Robust Control of a Closed loop identified system with parametric/model uncertainties and external disturbances ", Int'l Conf on Systems, Modeling and Simulations, January 27-29, 2010, Liverpool, UK.
  • R. Milne. Strategies for diagnosis. IEEE Trans. on Syst. , Man and Cyber. 17(3), 333—339 (1987).
  • R. J. Patton. Robustness in model-based fault diagnosis: the 1995 situation. A. Rev. Contr. 21, 103—123 (1997).
  • J. J. Gertler. Survey of model-based failure detection and isolation in complex plants. IEEE Contr. Syst. Mag. 8(6), 3—11 (1988).
  • G. Bastin and M. R. Gevers. Stable adaptive observers for nonlinear timevarying systems. IEEE Trans. on Automat. Contr. pages 650—658 (1988).
  • F. Hamelin and D. Sauter. Robust residual generation for FDI in uncertain dynamic systems. In "Proc. 34th IEEE Conf. on Decision & Contr. ", New Orleans, USA (1995)
  • Zhang, G. Q. P. , 2000. Neural networks for classification: a survey. IEEE Transactions on Systems, Man and Cybernetics: Part C—Applications and Reviews 30 (4), 451–462.
  • A. Khoukhi, S. Albukhitan, "PVT Properties Prediction Using Hybrid Genetic Neuro?Fuzzy Systems", ", International Journal of Oil Gas and Coal Technology, vol. 4, n0 1, 2011 pp: 47-63
  • A. Khoukhi: "Data-Driven Multi-Objective Motion Planning of Parallel Kinematic Machines", IEEE Trans. On Control System Technology, Vol. 18, No. 6, Nov. 2010 pp. 1381-1389,
  • A. Khoukhi, K. Benfreha: "Application of multi-layered neuro-fuzzy networks to collision detection in CAD-CAM processes", Trans. of the Canadian Society of Mech. Eng. Vol. 28, (3-4), (2004) pp. 431-443.
  • Wang, Y. , Chan, C. W. , Cheung, K. C. , 2001b. Intelligent fault diagnosis based on neuro-fuzzy networks for nonlinear dynamic systems. Proceedings of IFAC Conference on New Technologies for Computer Control 2001, Hong Kong, China, pp. 101–104.
  • de Miguel, L. J. , Bla´ zquez, L. F. , 2005. Fuzzy logic-based decision-making for fault diagnosis in a DC motor. Engineering Applications of Artificial Intelligence 18 (4), 423–450.
  • Mo-Yuen Chow, "Special Section on Motor Fault Detection and Diagnosis", IEEE Transaction on Industrial Electronics. 47 (5) (2000) 982–1107.
  • H. M. Khalid, and A. Khoukhi, "Hybrid Uscented Kalman Filter Neuiro Fuzzy Leak detection and Classification", Best Paper Award, Second Saudi Student Conf. Jeddah, March 28-30 2011.
  • M. A. Rahim, H. M. Khalid, M. Akram A. Khoukhi, , L. Cheded, and R. Doraiswami, "Quality Monitoring of a closed-loop system with parametric uncertainties and external disturbances: a fault Diagnosis Approach", Int'l Journal of Advanced Manufacturing Technology, vol. 55:293–306,
  • E. J. Henley. Application of expert systems to fault diagnosis. In "AIChE Annual Meeting", San Francisco, CA (1984).
  • K. Niida. Expert system experiments in processing engineering. In "Inst. Of Chem. Eng. Symposium Series", pp 529—583 (1985).
  • T. S. Ramesh, S. K. Shum, and J. F. Davis. A structured framework for efficient problem-solving in diagnostic expert systems. Computers and Chem. Eng. 12(9-10), 891—902 (1988).
  • T. S. Ramesh, J. F. Davis, and G. M. Schwenzer. Knowledge-based diagnostic systems for continuous process operations based upon the task framework. Computers and Chem. Eng. 16(2), 109—127 (1992).
  • V. Venkatasubramanian. CATDEX, knowledge-based systems in process engineering: Case studies in heuristic classification. Technical Report, The CACHE Corporation, Austin, TX (1989).
  • T. E. Quantrille and Y. A. Liu. "Artificial Intelligence in Chemical Engineering". Academic Press, San Diego, LA (1991).
  • C. Rojas Guzman and M. A. Kramer. Comparison of belief networks and rule-based systems for fault diagnosis of chemical processes. Eng. App. of Artificial Intelligence 3(6), 191—202 (1993).
  • M. Wo, W. Gui, D. Shen, and Y. Wang. Export fault diagnosis using role models with certainty factors for the leaching process. Proc. 3rd World Congress on Intelligent Contr. & Automation, vol 1, pp 238—241, Hefei, China (28 June-2 July 2000).
  • D. Leung and J. Romagnoli. Dynamic probabilistic model-based expert system for fault diagnosis. Computers and Chem. Eng. 24(11), 2473—2492 (2000).
  • N. J. Scenna. Some aspects of fault diagnosis in batch processes. Reliability Eng. and Syst. Safety 70(1), 95—110 (2000).
  • K. Watanabe, I. Matsura, M. Abe, M. Kubota, and D. M. Himmelblau. Incipient fault diagnosis of chemical processes via artificial neural networks. AICHE J. 35(11), 1803—1812 (1989).
  • V. Venkatasubramanian and K. Chan. A neural network methodology for process fault diagnosis. AICHE J. 35(12), 1993—2002 (1989).
  • L. H. Ungar, B. A. Powell, and S. N. Kamens. Adaptive networks for fault diagnosis and process control. Computers and Chem. Eng. 14(4-5), 561—572 (1990).
  • J. C. Hoskins, K. M. Kaliyur, and D. M. Himmelblau. Fault diagnosis in complex chemical plants using artificial neural networks. AICHE J. 37(1), 137—141 (1991).
  • V. Venkatasubramanian, R. Vaidyanathan, and Y. Yamamoto. Process fault detection and diagnosis using neural networks i: Steady state processes. Computers and Chem. Eng. 14(7), 699—712 (1990).
  • R. Vaidyanathan and V. Venkatasubramanian. Representing and diagnosing dynamic process data using neural networks. Eng. Applications of Artificial Intelligence 5(1), 11—21 (1992).
  • K. Watanabe, S. Hirota, L. Iloa, and D. M. Himmelblau. Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks. AICHE J. 40(5), 839—848 (1994).
  • J. Y. Fan, M. Nikolaou, and R. E. White. An approach to fault diagnosis of chemical processes via neural networks. AICHE J. 39(1), 82—88 (1993). 1
  • A. E. Farell and S. D. Roat. Framework for enhancing fault diagnosis capabilities of artificial neural networks. Computers and Chem. Eng. 18(7), 613—635 (1994).
  • C. S. Tsai and C. T. Chang. Dynamic process diagnosis via integrated neural networks. Computers and Chem. Eng. 19, s747—s752 (1995).
  • Y. Maki and K. A. Loparo. A neural-network approach to fault detection and diagnosis in industrial processes. IEEE Trans. on Contr. Syst. Technology 5(6), 529—541 (Nov 1997).
  • J. A. Leonard and M. A. Kramer. Diagnosing dynamic faults using modular neural nets. IEEE Expert 8(2), 44—53 (1993).
  • K. A. De Jong and W. M. Spears, Using genetic algorithms to solve NO-complete problems, in Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann: 1989.
  • D. E. Goldberg, K. Zakrzewski, B. Sutton, R. Gadient, C. Chang, P. Gallego, B. Miller and E. Cantu-Paz, Genetic Algorithms: A Bibliography, illegal Report no. 97011, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign