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.
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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
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DOI: https://doi.org/10.1007/3-540-57292-9_69
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