A major contribution to the field of FDD for vapor compression equipment could be made by a study that simply focused on developing a simple, robust, on-line training method of learning a mathematical model for normal system operating states. The development of a mathematical model and the specification of the training technique are viewed as a crucial step in the effort to apply FDD methods to existing vapor compression equipment. The results of this work will be used by a number of researchers in the field to improve their existing FDD methods and push this technology closer to widespread commercialization and application.

The objective of this research project is to develop and compare mathematical models that could be used to predict the fault-free operation of a vapor compression system and the on-line training techniques that allow this model to be trained during day-to-day operation. The findings of this study will be used as the basis for a follow-up study that will focus on demonstrating the most promising model and training technique at one or more field sites.

Principal Investigator: Agami Reddy, Drexel University

Conducted: Sept. 1999 - Oct. 2001

Sponsored by: TC 4.11, Smart Building Systems