Fault Detection and Diagnostics for Rooftop Air Conditioners

Discharge line temperature (F)
200
160
120
80
40
0
Transient data
Output of steady-state detector (steady-state=1, transient-state=0)
8/21/2001 6:40 8/22/2001 11:50 8/23/2001 11:02 8/24/2001 7:52
Time
3
2.5
2
1.5
1
0.5
0
Steady-state indicator
Figure 4.9 Steady-state detection
4.2.3 Clustering of steady-state data
For some problems, the number of data points may be small enough that all of the data
available can be used directly in learning a model. In other problems, the number of data
obtained can become sufficiently large that it is no longer practical to assign a separate
node to each data. Clustering techniques can be used to group data so that the group can
be represented by only one data point. Clustering of steady-state data is necessary for the
following two reasons:
Firstly, there are measurement noise and system disturbances, so clustering will act
as a filter.
Secondly, clustering will reduce the large number of steady state data to much a
smaller set of steady state data. This will greatly reduce the nodes of the GRNN
and memory to realize the GRNN algorithm.
First, establish a single radius of influence, r . Starting with the first point ( X ,Y ) ,
establish a cluster center,
i
X , at X . All future data for which the distance
than the distance to any other cluster center and is also
i . A data point for which the distance to the nearest cluster is larger than
i
X − X is less
≤ r would be grouped into cluster
r would become
the center for a new cluster. After clustering, the expectation (average) of each group data
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