Dynamic Bayesian Network for Fault Diagnosis

A comparative study between using a dynamic Bayesian network (DBN) against using a static Bayesian network (BN) for building heating ventilating, and air conditioning fault diagnosis (HVAC) is presented. Contrarily to a static BN, DBN method incorporates temporal dependencies between fault nodes between timesteps using temporal conditional probabilities. This allows fault beliefs to accumulate over time and hence improves the diagnosis accuracy. The two methods are evaluated using real building data obtained from a campus building. Overall, the DBN showed improved fault belief when diagnosing and isolating faults across various components and sub-systems. Sensitivity tests on the temporal conditional probabilities for DBN showed that the model is robust.