Title webinar:

Fault detection and diagnosis of HVAC systems

Description:

The Heating, Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total global energy consumption, a significant part of which is caused by incorrect operation of controls and incorrect maintenance. HVAC systems are becoming  increasingly complex,  especially  due  to  multi-commodity energy  sources, and as a result, the potential for failures  in systems  and  controls  will increase. That is why energy performance diagnostic systems are paramount. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. An important reason is  that  the  proposed  methods  differ  from  the  design  approaches of HVAC designers who use process and instrumentation diagrams (P&IDs).

The  so-called 4S3F method  made  a  step  forward  towards  a  systematic  and  automated  multi-system  and multi-level  fault  and  energy  performance  diagnosis  by  developing  an  energy  performance  FDD architecture  based  on  information  embedded  in  P&IDs.  This new approach combines systems theory with data analysis. In the 4S3F method, the symptoms and faults are classified into  4  types  of  symptoms  (balances, operating conditions (OS) and energy performances (EP) anomalies and additional information) and 3 types of faults (component, control and model faults). The detection (of symptoms) and diagnosis (identification of faults) phases are separated. The results of the symptom detection are imported in a DBN (Diagnostic Bayesian Network) method in which faults and symptom are linked in a network. The DBN software estimates the probabilities of the presence of faults.