Air Conditioning and Refrigeration Center. College of Engineering. University of Illinois at Urbana-Champaign.
Abstract
A fault detection and diagnosis (FDD) method was used to detect and diagnose
faults on both a refrigerator and an air conditioner during normal cycling operation. The
objective of the method is to identify a set of sensors that can detect faults reliably before
they severely hinder system performance. Unlike other methods, this one depends on the
accuracy of a number of small, on-line linear models, each of which is valid over a
limited range of operating conditions.
To detect N faults, N sensors are needed. Using M>N sensors can further reduce
the risk of false positives. For both the refrigerator and air conditioner systems, about
1000 combinations of candidate sensor locations were examined. Through inspection of
matrix condition numbers and each sensor's contribution to fault detection calculation, the
highest quality sets of sensors were identified. The issue of detecting simultaneous
multiple faults was also addressed, with varying success.
Fault detection was verified using both model simulations and experimental data.
The results were similar, although in practice only one of the two would probably be
used. Both load-type faults (such as door gasket leaks) and system faults were simulated
on the refrigerator. It was found that system faults were generally more easily detectable
than load faults.
Refrigerator experiments were performed on a typical household refrigerator
because it was readily available in a laboratory, but the results of this project may be
more immediately useful on larger commercial, industrial or transport refrigeration
systems. Air conditioner experiments were performed on a 3-ton split system. Again, the
economic benefits of this type of fault detection scheme may also be more feasible for
larger field-assembled systems.Air Conditioning and Refrigeration Project 8
