Name
Abstract | ||
|---|---|---|
In some domains like industry, medicine, communications, speech recognition, planning, tutoring systems, and forecasting; the timing of observations (symptoms, measures, test, events, as well as faults) play a major role in diagnosis and prediction. This paper introduces a new formalism to deal with uncertainty and time using Bayesian networks called Temporal Bayesian Network of Events (TBNE). In a TBNE each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relationship. A temporal node represents the time that a variable changes state, including an option of no-change. 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 subsystem of a thermal power plant, in which this approach is used for fault diagnosis and event prediction with good results. The TBNE model can be used for the diagnosis of a cascade of anomalies arising with certain delays; this situation is typical in the diagnosis of medical and industrial processes. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10489-005-3413-x | Appl. Intell. |
Keywords | Field | DocType |
bayesian networks,temporal uncertainty,diagnosis,prediction,industrial applications | Data mining,Variable-order Bayesian network,Computer science,Bayesian network,Artificial intelligence,Cascade,Formalism (philosophy),Granularity,Machine learning,Dynamic Bayesian network | Journal |
Volume | Issue | ISSN |
23 | 2 | 0924-669X |
Citations | PageRank | References |
12 | 0.75 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Arroyo-Figueroa | 1 | 170 | 22.16 |
| L. Enrique Sucar | 2 | 1016 | 118.72 |