Model-free actuator fault detection using a spectral estimation approach: The case of the DAMADICS benchmark problem

Control Engineering Practice

Volumn 14, Issue 6 SPEC. ISS., 2006, Pages 635-644

  Previdi, F ^a   Parisini, T ^b  

^a UNIVERSITY OF BERGAMO   (Italy)

^b UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Actuator fault detection; DAMADICS project; Model free fault detection; Spectral methods; Squared coherency function

Indexed keywords

BENCHMARKING; COMPUTER SIMULATION; CONDITION MONITORING; FAULT TREE ANALYSIS; MATHEMATICAL MODELS; SPECTRUM ANALYSIS;

ACTUATOR FAULT DETECTION; ALARM THRESHOLD; MODEL-FREE ACTUATOR FAULT DETECTION; SPECTRAL ESTIMATION; SQUARED COHERENCY FUNCTIONS;

ACTUATORS;

ACTUATORS; BENCHMARKING; COMPUTER SIMULATION; CONDITION MONITORING; FAULT TREE ANALYSIS; MATHEMATICAL MODELS; SPECTRUM ANALYSIS;

EID: 33644628220     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2005.04.001     Document Type: Article

References (20)

1. Bartys, M., Patton, R., Syfert, M., de las Heras, S., Quevedo, J. (2005). Introduction to the DAMADICS actuator FDI benchmark study. Control Engineering Practice, this issue.
2. Bartys, M., Syfert, M. (2002). Benchmark definition file. http://diag.mchtr.pw.edu.pl/damadics
3. J. Chen R.J. Patton Robust model-based fault diagnosis for dynamic systems 1999 Kluwer Academic Publishers Boston, Dordrecht, London
4. P.M. Frank Fault diagnosis in dynamic system using analytical and knowledge-based redundancy. A survey and some new results Automatica 26 1990 459-474
5. J.J. Gertler Survey of model-based failure detection and isolation in complex plants IEEE Control System Magazine 8 1988 3-11
6. J.J. Gertler Fault detection and diagnosis in engineering systems 1998 Marcel Dekker New York
7. G. Guglielmi T. Parisini G. Rossi Fault diagnosis and neural networks: A power plant application, Keynote Paper Control Engineering Practice 3 1995 601-620
8. Haber, R., 1985. Nonlinearity tests for dynamic processes. Proceedings of the IFAC identification and system parameter estimation, York, UK, pp. 409-413.
9. R. Isermann Process fault detection based on modeling and estimation methods. A survey Automatica 20 1984 387-404
10. G.M. Jenkins D.G. Watts Spectral analysis and its applications 1968 Holden-Day San Francisco
11. L. Ljung System identification 1999 Prentice-Hall Englewood Cliffs, NJ
12. P. Lohnberg Improved approximation of bias in squared coherency estimates for weakly smooth spectra IEEE Transactions on Acoustic, Speech, and Signal Processing ASSP-26 1978 172-174
13. A. Papoulis Minimum-bias windows for high-resolution spectral estimates IEEE Transactions on Information Theory IT-19 1 1973 9-12
14. R.J. Patton P.M. Frank R.N. Clark Fault diagnosis in dynamic systems theory and applications 1989 Prentice-Hall Englewood Cliffs, NJ
15. F. Previdi M. Lovera S. Mambretti Identification of the rainfall-runoff relationship in urban drainage networks Control Engineering Practice 7 12 1999 1489-1504
16. F. Previdi T. Parisini Model-free fault detection: A spectral estimation approach based on coherency functions International Journal of Control 74 11 2001 1107-1117
17. P. Stoica R. Moses Introduction to spectral analysis 1997 Prentice-Hall Englewood Cliffs, NJ
18. Vemuri, A. T., Polycarpou, M. M., 1996. On-line approximation based methods for robust fault detection. Proceedings of the 13th IFAC World congress (pp. 319-324).
19. A.S. Willsky A survey of design methods for failure detection in dynamic systems Automatica 12 1976 601-611
20. P. Young R.J. Patton Comparison of test signals for aircraft frequency domain identification Journal of Guidance, Control and Dynamics 13 3 1990 430-438