Abstract
This paper proposes a new approach for online fault diagnosis in dynamic systems, combining a Particle Filtering (PF) algorithm with a classic Fault Detection and Isolation (FDI) framework. Of the two methods, FDI provides deeper insight into a process; however, it cannot normally be computed online. Our approach uses a preliminary PF step to reduce the potential solution space, resulting in an online algorithm with the advantages of both methods. The PF step computes a posterior probability density to diagnose the most probable fault. If the desired confidence is not obtained, the classic FDI framework is invoked. The FDI framework uses recursive parametric estimation for the residual generation block and hypothesis testing and Statistical Process Control (SPC) criteria for the decision making block. We tested the individual methods with an industrial dryer.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Caccavale, F., Villani, L.: Fault Detection and Fault Tolerance for Mechatronics Systems: Recent Avances. Springer, Heidelberg (2003)
de Freitas, N.: Rao-Blackwellised particle filtering for fault diagnosis. In: IEEE Aerospace Conference (2001)
Doucet, A., Tadic, V.B.: B parameter estimation in general state-space models using particle methods. To appear Annals of the Institute of Statistical Mathematics 2003 (2003)
Doucet, A., de Freitas, N., Gordon, N.J.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Gabano, J.D.: Détection de defauts sur entraînement électrique. Technical report, Université de Poitiers, Poitiers, France (1993)
Gertler, J.: Fault detection and diagnosis in engineering systems. Marcel Dekker, Inc., New York (1998)
Ghahramani, Z., Hinton, G.E.: Parameter estimation for linear dynamical system. Technical Report CRG-TR-96-2, Department of Computer Science, University of Toronto, Toronto (1996)
Ghahramani, Z., Hinton, G.E.: Variational learning for switching state-space models. Neural Computation 12(4), 963–996 (1998)
Gordon, N., Salmond, D., Smith: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings-F 140(2), 107–113 (1993)
Hägglund, T., Aström, K.: Supervision of adaptive control algorithms. Automatica 36(1)
Hutter, F., Dearden, R.: Efficient on-line fault diagnosis for non-linear systems. In: 7th International Symposium on Artificial Intelligence, Robotics and Automation in Space (2003)
Hutter, F., Dearden, R.: The Gaussian particle filter for diagnosis of non-linear systems. In: 14th International Workshop on Principles of Diagnosis, Washington, DC (2003)
International Federation of Automatic Control. Fault detection, supervision and safety for technical process, Baden Baden, Germany (September 1992), Pergamon
International Federation of Automatic Control. Fault detection, supervision and safety for technical process, Espoo,Finland (June 1994), Pergamon
Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1987)
Morales-Menéndez, R., de Freitas, N., Poole, D.: Real-time monitoring of complex industrial processes with particle filters. In: Advances in Neural Information Processing Systems 16, MIT Press, Cambridge (2002)
Patton, R., Chen, J.: Parity space approach to model-based fault diagnosis. a tutorial survey and some results. In: IFAC SAFEPROCESS Symposium Baden-Baden, vol. 1, pp. 239–255 (1991)
Smith, C.A., Corripio, A.B.: Principles and Practice of Automatic Process Control, 2nd edn. John Wiley & Sons, Chichester (1997)
Valle, F.: Fault detection and isolation applied to the supervision of adaptive control systems; a neural network approach, Villa Erba, Italy (August 2001). International Federation of Automatic Control
Valle, F.: Statistical hypothesis neural network approach for fault detection and isolation an adaptive control system. In: MICAI, Merida, Mexico (Abril 2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Morales-Menéndez, R., Ramírez-Mendoza, R., Mutch, J., Guedea-Elizalde, F. (2004). Toward a New Approach for Online Fault Diagnosis Combining Particle Filtering and Parametric Identification. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_57
Download citation
DOI: https://doi.org/10.1007/978-3-540-24694-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
eBook Packages: Springer Book Archive