Abstract
A lot of previous approaches to monitoring involved a continuous reading of the system parameters in order to recognize when anomalies in the behavior of the system under examination can trigger the diagnostic process. This paper deals with the application of Markov chain theory to the selection of observation time in the monitoring and diagnosis of time-varying systems. The goal of the present paper is to show how, by assuming a framework where the temporal behavior of the components of the system is modeled in a stochastic way, the continuous observation of critical parameters can be avoided; indeed, this kind of approach allows us to get a useful criterion for choosing observation time in domains where getting observations can be expensive. Observations are then requested only when the necessity for a diagnostic process becomes relevant and a focusing on the components that are more likely to be faulty can also be achieved.
This work has been partially supported by CNR under grant n. 92.00061.CT12 and MURST.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
F. Bergadano, F. Brancadori, D. De Marchi, A. Giordana, S. Radicchi, and L. Saitta. Applying machine learning to troubleshooting: a case study in a real domain. In Proc. 10th Int. Conference on Expert Systems and Their Applications, pages 169–180, Avignon, 1990.
C. Berzuini, S. Quaglini, and R. Bellazzi. Temporal reasoning via bayesian networks. In Atti primo congresso AI * JA, pages 248–257, Trento, 1989.
L. Console, L. Portinale, D. Theseider Dupré, and P. Torasso. Diagnostic reasoning across different time points. In Proc. 10th ECAI, pages 369–373, Vienna, 1992.
L. Console, L. Portinale, D. Theseider Dupré, and P. Torasso. Diagnosing time-varying misbehavior: an apporoach based on model decomposition. Annals of Mathematics and Artificial Intelligence (to appear), 1993.
L. Console and P. Torasso. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence, 7(3):133–141, 1991. Also in [12].
J. de Kleer and B.C. Williams. Diagnosis with behavioral modes. In Proc. 11th IJCAI, pages 1324–1330, Detroit, 1989.
K. Downing. Consistency-based diagnosis in physiological domains. In Proc. AAAI-92, pages 558–563, San Jose, 1992.
J. Doyle and E. Sacks. Markov analysis of qualitative dynamics. Computational Intelligence, 7(1):1–10, 1992.
D. Dvorak and B. Kuipers. Model-based monitoring of dynamic systems. In Proc. 11th IJCAI, pages 1238–1243, Detroit, 1989.
G. Friedrich and F. Lackinger. Diagnosing temporal misbehaviour. In Proc. 12th IJCAI, pages 1116–1122, Sydney, 1991.
W. Hamscher. Modeling digital circuits for troubleshooting. Artificial Intelligence, 51(1–3):223–271, 1991.
W. Hamscher, L. Console, and J. de Kleer. Readings in Model-Based Diagnosis. Morgan Kaufmann, 1992.
W. Hamscher and R. Davis. Diagnosing circuit with state: an inherently underconstrained problem. In Proc. AAAI 84, pages 142–147, Austin, 1984.
L.J. Holtzblatt, M.J. Nejberg, R.L. Piazza, and M.B. Vilain. Temporal methods: multidimensional modeling of sequential circuits. In Proc. 2nd Int. Work. on Principles of Diagnosis, pages 111–120, Milano, 1991.
J.C. Kemeny and J.L. Snell. Finite Markov Chains. Springer Verlag, 1976.
F. Lackinger and W. Nejdl. Integrating model-based monitoring and diagnosis of complex dynamic systems. In Proc. 12th IJCAI, pages 1123–1128, Sydney, 1991.
M.K. Molloy. Discrete time stochastic Petri nets. IEEE Trans. on Software Engineering, SE-11(2):417–423, 1985.
M.F. Neuts. Probability. Allyn and Bacon, Inc., 1973.
L. Portinale. Modeling uncertain temporal evolutions in model-based diagnosis. In Proc. 8th Conf. on Uncertainty in Artificial Intelligence, pages 244–251, Stanford, 1992.
G.M. Provan. Modeling the dynamics of diagnosis and treatment using temporal influence diagrams. In Working Notes 3rd International Workshop on Principles of Diagnosis, pages 97–106, Rosario, WA, 1992.
K.S. Trivedi. Probability and Statistics with Reliability, Queueing and Computer Science Applications. Prentice-Hall, 1982.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Portinale, L. (1993). Selecting observation time in the monitoring and interpretation of time-varying data. In: Torasso, P. (eds) Advances in Artificial Intelligence. AI*IA 1993. Lecture Notes in Computer Science, vol 728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57292-9_69
Download citation
DOI: https://doi.org/10.1007/3-540-57292-9_69
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57292-3
Online ISBN: 978-3-540-48038-9
eBook Packages: Springer Book Archive