This work aims to study which sensors are required to be installed in a process in order to improve certain fault diagnosis specifications. Especially, the present method is based on structural models. Thus, system models involving a wide variety of equations (e.g. linear, non-linear algebraic, dynamics) can be easy handled. The use of structural models permits to define the diagnosis properties from the Dulmage-Mendelsohn decomposition, avoiding in this way the computation of any minimal redundant subsystem. Furthermore, in the present paper, the cost of the sensor configuration is considered. Therefore the proposed method attempts to find not all the possible solution but the optimal one. The optimal search is efficiently performed by developing an algorithm based on heuristic rules which, in general, allow to significantly reduce the search.
How to Cite
fault diagnosis, sensor placement, structural models
C. Commault, J.-M. Dion, and S. Yacoub Agha. Structural analysis for the sensor location problem in fault detection and isolation. In SAFE- PROCESS’2006, Beijing, China, Aug. 30th- Sep.1st 2006.
A. L. Dulmage and N. S. Mendelsohn. A structure theory of bi-partite graphs of finite exterior extension. Transactions of the Royal Society of Canada, 53(III):1–13, 1959.
Amir Fijany and Farrokh Vatan. A new efficient algorithm for analyzing and optimizing the system of sensors. In Proc. 2006 IEEE Aerospace Conference, Big Sky, Montana, USA, March 4– 11, 2006.
Mattias Krysander and Erik Frisk. Sensor placement for fault diagnosis. IEEE Trans. Syst., Man, Cybern. A, 38(6):1398–1410, 2008.
M. Krysander, J. Aslund, and M. . Nyberg. An efficient algorithm for finding minimal overconstrained subsystems for model-based-diagnosis. IEEE Trans. Syst., Man, Cybern. A, 38(1):197– 206, 2008.
F. Madron and V. Veverka. Optimal selection of measuring points in complex plants by lineair models. AICheE, 38(2):227–236, 1992.
D. Maquin, M. Luong, and J. Ragot. Fault detec- tion and isolation and sensor network design. European Journal of Automation, 31(2):393– 406, 1997.
K. Murota. Matrices and Matroids for Systems Analysis. Springer-Verlag, 2000.
S. Ploix, A. Yassine, and J.-M. Flaus. An improved algorithm for the design of testable subsystems. In The 17th IFAC World Congress, Seoul, Corea, 2008.
B. Pulido and C. Alonso. Possible conflicts, arrs, and conflicts. In 13th International Workshop on Principles of Diagnosis (DX02), pages 122– 128, May 2002.
A. Rosich, R. Sarrate, V. Puig, and T. Escobet. Efficient optimal sensor placement for model-based FDI using and incremental algorithm. In Proc. 46th IEEE Conference on Decision and Control, pages 2590–2595, New Orleans, USA, December 12–14, 2007.
A. Rosich, R. Sarrate, and F. Nejjari. Optimal sensor placement for FDI using binary integer linear programming. 20th International Workshop on Principles of Diagnosis, DX’09, 2009.
L. Trav ́e-Massuy`es, T. Escobet, and X. Olive. Diagnosability analysis based on component supported analytical redundancy relations. IEEE Trans. Syst., Man, Cybern. A, 36:1146 – 1160, 2006.
A. Yassine, S. Ploix, and J.-M. Flaus. A method for sensor placements taking into account diagnosability criteria. Applied Mathematics and Computer Science, 18(4), 2008.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.