Fault Detection and Diagnostics for Rooftop Air Conditioners

Since air conditioners always cycle, there is no doubt that some data which are “inside or
near the range of training data” will appear. Using these coming “inside or near the training
data” data, the FDD system can decide whether the system has a fault or not. If not, it can
be said safely that the stored data also are free of a fault, so they can be recruited into the
new training data and used to adapt the model.
Since so far the FDD is not developed, here only modeling adaptability is discussed and
the combination of FDD and modeling will be not discussed here. The benefit of
modeling adaptability can be simulated by adjusting the number of training data with the
total number of data points being constant. The simulation results are shown in figure 5.2
using laboratory data collected by Breuker (1997) which are organized as table 5.1. Figure
5.2 shows that the model performance is improved considerably with the expanding range
of training data.
Figure 5.1 Adaptive modeling scheme block diagram
56