Active Fault Diagnosis Based on Stochastic Tests

Niels Poulsen; Henrik Niemann

doi:10.2478/v10006-008-0043-6

.blurhash-client-img { display: none !important; }

Active Fault Diagnosis Based on Stochastic Tests

International Journal of Applied Mathematics and Computer Science

Volume 18 (2008): Issue 4 (December 2008)

By: Niels Poulsen and Henrik Niemann

Open Access

|Dec 2008

Abstract

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.

References

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