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State-Space GMDH Neural Networks for Actuator Robust Fault DiagnosisMRUGALSKI, M. , WITCZAK, M.
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fault diagnosis, robustness, actuators, neural networks, system identification
fault(14), control(12), systems(11), neural(10), witczak(7), networks(7), estimation(7), mrugalski(6), korbicz(6), gmdh(6)
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About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03010
Web of Science Accession Number: 000308290500010
SCOPUS ID: 84865856659
Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.
|References|||||Cited By «-- Click to see who has cited this paper|
| R. Aarenstrup, "DC motor model," June 2012. [Online] Available: Temporary on-line reference link removed - see the PDF document
 D. Berdjag, V. Cocquempot, C. Christophe, A. Shumsky, and A. Zhirabok, "Algebraic approach for model decomposition: application to fault detetion and isolation in discrete-event systems," International Journal of Applied Mathematics and Computer Science, vol. 21, pp. 109-125, 2011.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]
 M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control. Berlin, Heidelberg, New York: Springer-Verlag, 2003.
 S. De Oca, V. Puig, M. Witczak, and £. Dziekan, "Fault-tolerant control strategy for actuator faults using lpv techniques: Application to a two degree of freedom helicopter," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 1, pp. 161-171, 2012.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 48]
 S. Ding, Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Berlin/Heidelberg: Springer-Verlag, 2008.
 S. Gillijns and B. D. Moor, "Unbiased minimum-variance input and state estimation for linear discrete-time systems," Automatica, vol. 43, pp. 111-116, 2007.
[CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 145]
 S. Haykin, Kalman Filtering and Neural Networks. New York: John Wiley & Sons, 2001.
 S. Haykin, Neural Networks and Learning Machines. New York: Prentice Hall, 2009.
 R. Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Heidelberg/Berlin: Springer-Verlag, 2005.
 A. Ivakhnenko and J. Mueller, "Self-organization of nets of active neurons," System Analysis Modelling Simulation, vol. 20, pp. 93-106, 1995.
 S. Julier and J. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
[CrossRef] [Web of Science Times Cited 1881] [SCOPUS Times Cited 3130]
 T. Kondo and J. Ueno, "Nonlinear system identification by feedback gmdh-type neural network with architecture self-selecting function," in Intelligent Control (ISIC), 2010 IEEE International Symposium on, sept. 2010, pp. 1521-1526.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 1]
 J. Korbicz and M. Mrugalski, "Confidence estimation of gmdh neural networks and its application in fault detection system," International Journal of System Science, vol. 39, no. 8, pp. 783-800, 2008.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 18]
 J. Korbicz, M. Witczak, and V. Puig, "Lmi-based strategies for designing observers and unknown input observers for non-linear discrete-time systems," Technical Sciences, vol. 55, no. 1, pp. 31-42.
 L. Kral and M. Simandl, "Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network," Adaptive Control and Signal Processing, vol. 25, pp. 949-964, 2011.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7]
 T. Lee and Z. Jiang, "On uniform global asymptotic stability of nonlinear discrete-time systems with applications," IEEE Trans. Automatic Control, vol. 51, no. 10, pp. 1644-1660, 2006.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 22]
 L. Ljung, System Identification: Theory for the User. Upper Saddle River, New York: Prentice Hall PTR, 1999.
 M. Mrugalski and J. Korbicz, "Gmdh neural networks," in The Industrial Electronics Handbook, 2nd ed., B. Wilamowski and J. Irwin, Eds. Boca Raton: CRC Press, Taylor Francis Group, 2011, vol. 5, pp. 8-1-8-21.
 M. Mrugalski, J. Korbicz, and R. Patton, "Robust fault detection via gmdh neural networks," Proceedings of 16th IFAC World Congress, IFAC, 2005.
 M. Mrugalski and M. Witczak, "Parameter estimation of dynamic gmdh neural networks with the bounded-error technique," J. Appl. Comput. Sci, vol. 10, no. 1, pp. 77-90, 2002.
 M. Mrugalski, M. Witczak, and J. Korbicz, "Confidence estimation of the multi-layer perceptron and its application in fault detection systems," Engineering Applications of Artificial Intelligence, vol. 21, no. 6, pp. 895-906, 2008.
 H. Niemann, "A model-based approach to fault-tolerant control," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 1, pp. 67-86, 2012.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 22]
 H. Noura, D. Theilliol, J. Ponsart, and A. Chamseddine, Fault-tolerant Control Systems: Design and Practical Applications. London: Springer-Verlag, 2009.
 R. Patton, P. Frank, and R. Clark, Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Berlin: Springer-Verlag, 2000.
 I. Peddle, "Discrete state space control," Control Systems, vol. 414, pp. 2-3, 2007.
 T. Senguler and E. K. amd S. Seker, "A new mlp approach for the detection of the incipient bearing damage," Advances in Electrical and Computer Engineering, vol. 10, no. 3, pp. 34-39, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]
 O. Straka, J. Dunik, and M. Simandl, "Truncation nonlinear filters for state estimation with nonlinear inequality constraints," Automatica, vol. 48, pp. 273-286, 2012.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23]
 B. Teixeira, L. Torres, L. Aguirre, and D. Bernstein, "On unscented kalman filtering with state interval constraints," Journal of Process Control, vol. 20, no. 1, pp. 45-57, 2010.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 42]
 M. Witczak, Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems. From Analytical to Soft Computing Approaches. Berlin: Springer-Verlag, 2007.
 M. Witczak, J. Korbicz, M. Mrugalski, and R. Patton, "A gmdh neural network based approach to robust fault detection and its application to solve the damadics benchmark problem," Control Engineering Practice, vol. 14, no. 6, pp. 671-683, 2006.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 63]
 M. Witczak and P. Pretki, "Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms," International Journal of Control, vol. 80, no. 5, pp. 749-762, 2007.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 31]
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