Uncertainty in model-based condition monitoring

Abstract

Model-based techniques for automated condition monitoring of HVAC systems have been under development for some years. The generation of false alarms has been identified as a principal factor affecting the potential usefulness of condition monitoring in HVAC applications. Results from the application of these methods to systems installed in real buildings have highlighted the difficulty in selecting good alarm thresholds that balance robustness (lack of false alarms) and sensitivity (early detection). This paper demonstrates that this balance can be met in a transparent and analytical manner, through the application of uncertainty analysis. The paper discusses the sources of uncertainty associated with component models and system measurements. A condition monitoring scheme applied to a typical HVAC cooling coil subsystem installed in a real building is presented. Faults are artificially introduced into the system and are used in conjunction with fault-free operation to demonstrate the sensitivity and robustness of the scheme. The principle conclusions drawn by the paper consider the likely minimum magnitudes of faults that can be detected in typical HVAC systems, without false alarm generation. More broadly however, the paper demonstrates that the issue of uncertainty affects all aspects of system monitoring, modelling and control.