Fault Detection and Diagnostics for Rooftop Air Conditioners

3.2 Comparisons
Four black-box modeling approaches: polynomials, GRNN, RBF and BP neural networks
were investigated using the laboratory data. Seven characteristic parameters (evaporating
temperature , condensing temperature T , compressor discharge temperature
, suction line superheat T , liquid line subcooling T , condenser air temperature
difference
T evap
∆ , evaporator air temperature difference ∆ ) for FDD were modeled.
T ca
cond
Tdis
sh sc
Since the gathered data are very limited (94 points for large set data and 40 points for small
set data), when testing interpolation, the large data set were used to train models and both
the small and large data set were used to test models. When testing extraplation, the core of
the total data (T from 73 to 79 F,T from 70 to 90 F, T from 58 to 64 F) were used
to train models and the remaining data were used to test models.
To get a visual feeling of the modeling performance, figure 3.3 and figure 3.4 show the
training performance and testing performance for evaporating temperature. Table 3.2 and
figure 3.5 show the RMS error for the polynomial models. Polynomial models have good
interpolating ability when the order is high enough (e.g. third order) and the interpolating
performance increases as the order increases. However, low-order polynomial models have
good extrapolating performance, while the extrapolating performance will be very poor
when the polynomial order is too high (e.g. the third order). So there exists a conflict
between interpolating and extrapolating performance.
Table 3.2 and figure 3.6 show that the GRNN models have very good interpolating ability
but poor extrapolating performance. The spread has a significant influence on the
interpolating performance but little influence on extrapolating performance. The smaller
the spread, the better the interpolating performance. Another advantage of GRNN is that
training is very fast. The disadvantage of GRNN is that large memory is required to record
the nodes when the number of nodes is large.
From table 3.3, it can be seen that the BP neural network has very good interpolating
ability when the number of neurons is appropriate, but extrapolating performance is poor.
Also the performance is a little random, because the initial condition is random. The
weakness of the BP neural network is that it takes a little long time to train.
T ea
ra amb wb
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