Active fault diagnosis based on stochastic tests

• Autorzy: Niels K. Poulsen , Henrik Niemann
• Języki publikacji: EN

Abstrakty

EN

The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

Słowa kluczowe

EN

active fault diagnosis; parametric faults; stochastic change detection; closed-loop systems; parameterization

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 4
• Strony: 487-496

Daty

wydano

2008

otrzymano

2007-10-10

poprawiono

2008-01-29

Twórcy

autor

Niels K. Poulsen

• Department of Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK-2800 Kgs. Lyngby, Denmark

autor

Henrik Niemann

• Department of Electrical Engineering, Automation and Control, Technical University of Denmark, Building 326, DK-2800 Kgs. Lyngby, Denmark

Bibliografia

• Basseville M. and Nikiforov I. (1993). Detection of Abrupt Changes: Theory and Application, Prentice Hall, Englewood Cliffs, NJ.
• Campbell S., Horton K. and Nikoukhah R. (2002). Auxiliary signal design for rapid multi-model identification using optimization, Automatica 38(8): 1313-1325.
• Campbell S., Horton K., Nikoukhah R. and Delebecque F. (2000). Rapid model selection and the separability index, Proceedings of the IFAC Symposium Safeprocess 2000, Budapest, Hungary, pp. 1187-1192.
• Campbell S. and Nikoukhah R. (2004a). Auxiliary Signal Design for Failure Detection, Princeton University Press, Princeton, NJ.
• Campbell S. and Nikoukhah R. (2004b). Software for auxiliary signal design, Proceedings of the American Control Conference, Boston, MA, USA, pp. 4414-4419.
• Frank P. and Ding X. (1994). Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis, Automatica 30(5): 789-804.
• Gustafsson F. (2000). Adaptive Filtering and Change Detection, Wiley & Sons, Chichester.
• Kerestecioglu F. (1993). Change Detection and Input Design in Dynamic Systems, Research Studies Press, Baldock, Hertfordshire.
• Kerestecioglu F. and Cetin I. (2004). Optimal input design for detection of changes towards unknown hypoteses, International Journal of System Science 35(7): 435-444.
• Kerestecioglu F. and Zarrop M. (1994). Input design for detection of abrupt changes in dynamical systems, International Journal of Control 59(4): 1063-1084.
• Niemann H. (2003). Dual Youla parameterization, IEE Proceedings - Control Theory and Applications 150(5): 493-497.
• Niemann H. (2005). Fault tolerant control based on active fault diagnosis, Proceedings of the American Control Conference, Portland, OR, USA, pp. 2224-2229.
• Niemann H. (2006). A setup for active fault diagnosis, IEEE Transactions on Automatic Control 51(9): 1572-1578.
• Niemann H. and Poulsen N. (2005). Active fault diagnosis in closed-loop systems, Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, (on DVD).
• Nikoukhah R. (1994). Innovations generation in the presence of unknown inputs: Application to robust failure detection, Automatica 30(12): 1851-1867.
• Nikoukhah R. (1998). Guaranteed active failure detection and isolation for linear dynamical systems, Automatica 34(11): 1345-1358.
• Nikoukhah R., Campbell S. and Delebecque F. (2000). Detection signal design for failure detection: A robust approach, International Journal of Adaptive Control and Signal Processing 14: 701-724.
• Poulsen N. and Niemann H. (2007). Stochastic change detection based on an active fault diagnosis approach, Proceedings of the 8th Conference on Diagnostics of Processes and Systems, DPS 2007, Słubice, Poland, pp. 113-120.
• Tay T., Mareels I. and Moore J. (1997). High Performance Control, Birkhäuser, Boston, MA.
• Zhang X. (1989). Auxiliary Signal Design in Fault Detection and Diagnosis, Springer Verlag, Heidelberg.

Identyfikatory

DOI

10.2478/v10006-008-0043-6