A GMDH neural network based approach to passive robust fault detection using a constraints satisfaction backward test

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 38, Issue 1, 2005, Pages 203-208

  Puig, V ^a   Mrugalski, M ^b   Ingimundarson, A ^a   Quevedo, J ^a   Witczak, M ^b   Korbicz, J ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Adaptive threshold; Fault detection; Fault diagnosis; Neural network; Robustness

Indexed keywords

DATA HANDLING; FAILURE ANALYSIS; NEURAL NETWORKS; NONLINEAR SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS); UNCERTAINTY ANALYSIS;

ADAPTIVE THRESHOLDS; CONSTRAINTS SATISFACTION; GMDH NEURAL NETWORK; GROUP METHOD OF DATA HANDLING; INTERVAL CONSTRAINT SATISFACTION; PARAMETER UNCERTAINTY; ROBUST FAULT DETECTION; UNCERTAIN PARAMETERS;

FAULT DETECTION;

EID: 79960729772     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20050703-6-cz-1902.01836     Document Type: Conference Paper

References (17)

1. Armengol, J., Vehi, J., Travé-Massuyès, L. and M. A. Sainz (2000). Interval Model-Based Fault Detection using Multiple Sliding Windows. IFAC SAFEPROCESS'00, Budapest, Hungary, pp. 168-173.
2. Chen, J. and R. J. Patton (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers.
3. DAMADICS (2004). Website RTN DAMADICS: Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems. http://diag.mchtr.pw.edu.pl/damadics.
4. Escobet, T., Travé, L., Tornil, S. and J. Quevedo (2001). Fault Detection of a Gas Turbine Fuel Actuator Based on Qualitative Causal Models. ECC'01. Portugal.
5. Hyvönen, E. (1992). Constraint reasoning based on interval arithmetic: The tolerance approach. Artificial Intelligence, 58, pp. 71-112.
6. Ivakhnenko, A. G. and J. A. Mueller (1995). Self-organizing of nets of active neurons. System Analysis Modelling Simulation, 20, pp. 93-106.
7. Jaulin, L., Kieffer, M., Didrit, O. and E. Walter (2001). Applied Interval Analysis, with Examples in Parameter and State Estimation, Robust Control and Robotics. Springer-Verlag. London.
8. Korbicz, J., Kościelny, J. M., Kowalczuk, Z. and W. Cholewa (Eds.) (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, Berlin.
9. Milanese, M., Norton, J., Piet-Lahanier, H., and E. Walter (Eds). (1996). Bounding Approaches to System Identification. Plenum Press.
10. Mrugalski, M. (2004). Neural Network Based Modelling of Non-linear Systems in Fault Detection Schemes. Zielona Góra, Faculty of Electrical Engineering, Computer Science and Telecommunications, University of Zielona Góra, doctoral dissertation. (In Polish).
11. Ploix, S., Adrot, O. and J. Ragot (2000). Bounding Approaches to the Diagnosis of Uncertain Static Systems. IFAC SAFEPROCESS'00, Budapest, Hungary, pp. 149-154.
12. Puig, V., Quevedo, J., Escobet, T., S. De las Heras (2002). Robust Fault Detection Approaches using Interval Models. IFAC World Congress (b'02). Barcelona. Spain, CD-ROM.
13. Patton, R. J. and J. Korbicz (Eds.) (1999). Advances in computational intelligence for fault diagnosis systems. Applied Mathematical and Computer Science, special issue, 9(3), pp. 468-735.
14. Travé-Massuyès, L. and R. Milne (1997). TIGER: Gas turbine condition monitoring using qualitative model based diagnosis. IEEE Expert Intelligent Systems and Applications, May-June.
15. Waltz, D. (1975). Understanding Line Drawings of Scenes with Shadows. P.H. Winston Ed. The Psychology of Computer Vision. McGraw-Hill, New York, pp. 19-91.
16. Witczak, M., Korbicz, J., Mrugalski, M. and R. J. Patton (2005). A GMDH neural network based approach to robust fault detection and its application to solve the DAMADICS benchmark problem. Control Engineering Practice (accepted).
17. Witczak, M. and M. Mrugalski (2003). Designing GMDH Neural Networks with the Bounded-Error Approach: Application to Robust Fault Detection. Proc. 9th IEEE Inter. Conf. Methods and Models in Automation and Robotics, MMAR 2003, Miedzyzdroje, Poland, pp. 1377-1382.