Fault Detection and Diagnostics for Rooftop Air Conditioners

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EXECUTIVE SUMMARY
Packaged air conditioning equipment is used extensively throughout small
commercial and institutional buildings. However, compared to larger systems, they tend
to be not well maintained. Widespread application of automated fault detection and
diagnosis (FDD) to packaged equipment will significantly reduce energy use and peak
electrical demand, down time and maintenance costs. However, techniques for online
FDD reported in the literature are expensive to apply because of requirements for model
training and large computation, have only been tested in the laboratory, and can not
handle multiple-simultaneous faults. Furthermore, no one has performed economic
assessments of FDD. Economic assessment is a complicated problem that requires field
evaluations. The primary goals of the research described in this report were to 1) develop
a practical automated FDD technique having low cost, robust performance, and the
capability to handle multiple-simultaneous faults and 2) to perform an initial economic
assessment of FDD applied to vapor compression equipment in California.
The process of developing and evaluating the performance of FDD methods
involved the use of both laboratory and field data. Laboratory data from previous studies
were used to provide rigorous performance evaluations. In addition, a number of field
sites were established in small commercial buildings in California to allow consideration
of practical issues. An additional field site was set up at Purdue to allow artificial
implementation of faults in order to provide more controlled evaluation of the FDD
techniques under realistic operating conditions.
Two different FDD approaches were developed in this research. First of all, the
statistical rule-based (SRB) FDD method presented by Rossi and Braun (1997) was
modified to improve sensitivity and robustness and reduce computational requirements.
The following components of the FDD method were improved: 1) models for predicting