A Structural Approach to Sensor Placement based on Symbolic Compilation of the Model

  • Gianluca Torta
  • Pietro Torasso
Conference paper


In the present paper we address the problem of computing the Minimal Additional Sensor Sets (MASS) that guarantee a desired level of diagnostic discrimination for a system.

The main contribution of this paper is the extension and the adaptation of techniques based on the symbolic compilation of qualitative system models to a structural approach suitable for the computation of MASS for component-oriented models consisting of sets of numerical equations. In this respect, the paper can be viewed as a bridge across the AI approaches to model-based sensor placement and the Fault Detection and Isolation approaches developed by the Automatic Control community. We show that the resulting method exploits the symbolic compilation techniques not only as a way to provide computational savings (including some theoretical guarantees on the computational complexity), but it also exhibits interesting new features, most notably the handling of multiple faults.


Exogenous Variable Endogenous Variable Relational Algebra Minimum Cardinality Domain Theory 
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  1. 1.
    Cordier, M.O., Travé-Massuyès, L., Pucel, X.: Comparing Diagnosability in Continuous and Discrete-Event Systems. In: Proc. Proc. DX, pp. 55-60, (2006)Google Scholar
  2. 2.
    Cassar, J.P., Staroswiecki, M.: A Structural Approach for the Design of Failure Detection and Identification Systems. In: Proc. IFAC Control of Industrial Systems (1997)Google Scholar
  3. 3.
    Travé-Massuyès, L., Escobet, T., Olive, X.: Diagnosability Analysis Based on Component- Supported Analytical Redundancy Relations. IEEE Tr. on Systems, Man and Cybernetics PART A 36(6), 1146–1160 (2006)CrossRefGoogle Scholar
  4. 4.
    Krysander, M., Frisk, E.: Sensor placement for fault diagnosis. IEEE Tr. on Systems, Man and Cybernetics PART A 38(6), 1398–1410 (2008)CrossRefGoogle Scholar
  5. 5.
    Commault, C., Dion, J., Agha, S.: Structural analysis for the sensor location problem in fault detection and isolation. In: Proc. IFAC World Congress, pp. 949-954 (2006)Google Scholar
  6. 6.
    Travé-Massuyès, L., Escobet, T., Milne, R.: Model-based Diagnosability and Sensor Placement. Application to a Frame 6 Gas Turbine Sub-System. In: Proc. IJCAI, pp. 551-556 (2001)Google Scholar
  7. 7.
    Torta, G., Torasso, P.: Computation of Minimal Sensor Sets from Precompiled Discriminability Relations. In: Proc. DX, pp. 202–209 (2007)Google Scholar
  8. 8.
    Torasso, P., Torta, G.: Model-Based Diagnosis through OBDD Compilation: a Complexity Analysis. LNCS 4155, 287-305 (2006)Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

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