Fault diagnosis method of variable air volume terminals based on Bayesian network

Aiming at 15 typical faults of the pressure independent variable air volume (VAV) terminal with reheat coil, proposes a Bayesian network-based fault diagnosis method (FDD). Establishes a Dymola simulation model based on an actual VAV system, and verifies the performance of the proposed method based on simulated fault data. The results show that this method performs well at: (1) detecting and isolating most faults with high accuracy and reliability, (2) dealing with the data problems existing in actual engineering, (3) further popularizing the application of real-time fault diagnosis.