Probabilistic Reasoning with Temporal Nodes and its Applications for Diagnosis and Prediction of Events
Gustavo Figueroa · Computación y Sistemas · 2009
DIAGNOSIS AND PREDICTION IN SOME DOMAINS, LIKE MEDICAL AND INDUSTRIAL DIAGNOSIS, REQUIRE A REPRESENTATION THAT COMBINES UNCERTANTY MANAGEMENT AND TEMPORAL REASONING. BASED ON THE FACT THAT IN MANY CASES THERE ARE STATE CHANGES IN THE TEMPORAL RANGE OF INTEREST, WE PROPOSE A NOVEL REPRESEN-TATION CALLED TEMPORAL NODES BAYESIAN NETWORK (TNBN) BASED ON THE FACT THAT IN MANY CASES THERE ARE FEW STATE CHANGES IN THE TEMPORAL RANGE OF INTEREST. IN A TNBN EACH NODE REPRESENTS AN EVENT OR STATE CHANGE OF A VARIABLE, AND AN ARC CORRESPONDS TO A CAUSAL-TEMPORAL RALETION. THE TEMPORAL INTERVALS CAN DIFFER IN NUMBER AND SIZE FOR EACH TEMPORAL NODE, SI THIS ALLOWS MULTIPLE GRANULARITY. OUR APPROACH IS CONTRASTED WITH A DYNAMIC BAYESIAN NETWORK FOR SIMPLE MEDICAL EXAMPLE. AN EMPIRICAL EVALUATION IS PRESENTED FOR MORE COMPLEX PROBLEM, A SUBSYSTEM OF A FOSSIL POWER PLANT, IN WHICH THIS APPROACH IS USED FOR FAULT DIAGNOSIS AND EVENT PREDICTION WITH GOOD RESULTS.