A Temporal Bayesian Network for Diagnosis and Prediction
This work addresses diagnosis and prediction challenges in medical and industrial domains, but it appears incremental as it builds on existing Bayesian network methods with a focus on temporal aspects.
The authors tackled the problem of diagnosis and prediction in domains like medicine and industry by proposing Temporal Nodes Bayesian Networks (TNBN), a representation that combines uncertainty management and temporal reasoning, and demonstrated good results in fault diagnosis and prediction for a fossil power plant subsystem.
Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Networks (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relationship. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and prediction with good results.