118 research outputs found
Selecting Observation time in the monitoring and interpretation of Time-Varying data
No full textA 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
A comparative analysis of Horn models and Bayesian Networks for diagnosis
No full textThe aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the framework of diagnostic reasoning. This is pursued by pointing out similarities between the two formalisms at the modeling level and by introducing into BNs a suitable notion of derivation. We also discuss modeling issues underlying the choice of Horn-based models vs BNs, by making explicit the “completion semantics” underlying a BN. This correspondence between “completed” Horn theories and BNs allows us to formally justify classical diagnostic schemata adopted for BNs
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