A layered, any time approach to sensor validation

  • Pablo H. Ibargüengoytia
  • Sunil Vadera
  • L. Enrique Sucar
Accepted Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)


Sensors are the most usual source of information in many automatic systems such as automatic control, diagnosis, monitoring, etc. These computerised systems utilise different models of the process being served which usually, assume the value of the variables as a correct reading from the sensors. Unfortunately, sensors are prone to failures. This article proposes a layered approach to the use of sensor information where the lowest layer validates sensors and provides the information to the higher layers that model the process. The proposed mechanism utilises belief networks as the framework for failure detection, and uses a property based on the Markov blanket to isolate the faulty sensors from the apparently faulty sensors. Additionally, an any time version of the sensor validation algorithm is presented and the approach is tested on the validation of temperature sensors in a gas turbine of a power plant.


Uncertainty Belief networks sensor validation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Basseville. Detecting changes in signals and systems. Automatica, 24(3):309–326, 1988.Google Scholar
  2. 2.
    T. Dean and M. Boddy. An analysis of time dependent planning. In Proc. Seventh Natl. Conf. on AI, St. Paul, MN, U.S.A., 1988.Google Scholar
  3. 3.
    T. Dean and M.P. Wellman. Planning and control. Morgan Kaufmann, Palo Alto, Calif., U.S.A., 1991.Google Scholar
  4. 4.
    J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In A. Prieditis and S. Russell, editors, Machine Learning, Proceedings of the Twelfth International Conference, San Francisco, CA, U.S.A., 1995. Morgan Kaufmann.Google Scholar
  5. 5.
    E. Driver and D. Morrell. Implementation of continuous bayesian networks using sums of weighted gaussians. In Proc. Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, 1995.Google Scholar
  6. 6.
    M. Henrion, J.S. Breese, and E.J. Horvitz. Decision analysis and expert systems. AI Magazine, Winter:64–91, 1991.Google Scholar
  7. 7.
    M.P. Henry and D.W. Clarke. The self-validating sensor: rationale, definitions and examples. Control Engineering Practice, 1(4):585–610, 1993.Google Scholar
  8. 8.
    P.H. Ibargüengoytia, L.E. Sucar, and S. Vadera. A probabilistic model for sensor validation. In Proc. Twelfth Conference on Uncertainty in Artificial Intelligence, pages 332–339, Portland, Oregon, U.S.A., 1996.Google Scholar
  9. 9.
    R. Milne and C. Nicol. Tiger: knowledge based gas turbine condition monitoring. AI Communications, 9:92–108, 1996.Google Scholar
  10. 10.
    J. Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, Palo Alto, Calif., U.S.A., 1988.Google Scholar
  11. 11.
    L.E. Sucar, J. Pérez-Brito, and J.C. Ruiz-Suarez. Induction of dependence structures from data and its application to ozone prediction. In G.F. Forsyth and M. Ali, editors, Procedings Eight International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE), pages 57–63, DSTO:Australia, 1995.Google Scholar
  12. 12.
    S.K. Yung and D.W. Clarke. Local sensor validation. Measurement & Control, 22(3):132–141, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Pablo H. Ibargüengoytia
    • 1
  • Sunil Vadera
    • 1
  • L. Enrique Sucar
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of SalfordSalford
  2. 2.Campus MorelosITESMCuernavaca, Mor.Mexico

Personalised recommendations