Abstract
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.
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References
M. Basseville. Detecting changes in signals and systems. Automatica, 24(3):309–326, 1988.
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.
T. Dean and M.P. Wellman. Planning and control. Morgan Kaufmann, Palo Alto, Calif., U.S.A., 1991.
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.
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.
M. Henrion, J.S. Breese, and E.J. Horvitz. Decision analysis and expert systems. AI Magazine, Winter:64–91, 1991.
M.P. Henry and D.W. Clarke. The self-validating sensor: rationale, definitions and examples. Control Engineering Practice, 1(4):585–610, 1993.
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.
R. Milne and C. Nicol. Tiger: knowledge based gas turbine condition monitoring. AI Communications, 9:92–108, 1996.
J. Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, Palo Alto, Calif., U.S.A., 1988.
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.
S.K. Yung and D.W. Clarke. Local sensor validation. Measurement & Control, 22(3):132–141, 1989.
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© 1997 Springer-Verlag Berlin Heidelberg
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Ibargüengoytia, P.H., Vadera, S., Sucar, L.E. (1997). A layered, any time approach to sensor validation. In: Gabbay, D.M., Kruse, R., Nonnengart, A., Ohlbach, H.J. (eds) Qualitative and Quantitative Practical Reasoning. FAPR ECSQARU 1997 1997. Lecture Notes in Computer Science, vol 1244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035633
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DOI: https://doi.org/10.1007/BFb0035633
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