Model based techniques for automated condition monitoring of HVAC systems have been
under development for some years. Results from the application of these methods to systems
installed in real buildings have highlighted robustness and sensitivity issues. The
generation of false alarms has been identified as a principal factor affecting the potential
usefulness of condition monitoring in HVAC applications. The robustness issue is a direct
result of the uncertain measurements and the lack of experimental control that axe
characteristic of HVAC systems. This thesis investigates the uncertainties associated with
implementing a condition monitoring scheme based on simple first principles models in
HVAC subsystems installed in real buildings.
The uncertainties present in typical HVAC control system measurements are evaluated.
A sensor validation methodology is developed and applied to a cooling coil subsystem installed
in a real building. The uncertainty in steady-state analysis based on transient data
is investigated. The uncertainties in the simplifications and assumptions associated with
the derivation of simple first principles based models of heat-exchangers are established. A
subsystem model is developed and calibrated to the test system. The relationship between
the uncertainties in the calibration data and the parameter estimates are investigated. The
uncertainties from all sources are evaluated and used to generate a robust indication of
the subsystem condition. The sensitivity and robustness of the scheme is analysed based
on faults implemented in the test system during summer, winter and spring conditions.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.