Efficient Optimal Sensor Placement for Structural Model Based Diagnosis



Published Oct 11, 2010
Albert Rosich Abed Alrahim Yassine St ́ephane Ploix


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

Rosich, A. ., Yassine , A. A., & Ploix S. ́. . (2010). Efficient Optimal Sensor Placement for Structural Model Based Diagnosis. Annual Conference of the PHM Society, 2(2). https://doi.org/10.36001/phmconf.2010.v2i1.1943
Abstract 117 | PDF Downloads 116



fault diagnosis, sensor placement, structural models

M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki. Diagnosis and Fault-tolerant Control. Springer-Verlag, 2006.

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.
Technical Research Papers