A Temporal Bayesian Network for Diagnosis and Prediction

TL;DR

This paper introduces Temporal Nodes Bayesian Networks (TNBN), a novel probabilistic model that captures temporal events and causal relationships for diagnosis and prediction in domains with limited state changes.

Contribution

The paper presents TNBN, a new representation that handles temporal reasoning and uncertainty, with variable temporal granularity, outperforming traditional dynamic Bayesian networks in complex fault diagnosis tasks.

Findings

Effective in fault diagnosis of power plant subsystems

Handles multiple temporal granularities

Shows good results compared to existing methods

Abstract

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.