Fault Detection and Diagnostics for Rooftop Air Conditioners

5. Adaptive Polynomial plus GRNN
Although the polynomial plus GRNN model has excellent interpolating performance and
good extrapolating performance in the range of the training data, the extrapolating
performance far outside the range of the training data is not guarantied, especially for,
1. Noisy training data, which is a characteristic of measurement data
2. Sparse training data, which is normal when FDD is first commissioned
3. Range-limited training data, which is a normal case for field data
4. Strong nonlinear area where dry conditions change to wet conditions
5. Limited nodes, which will reduce memory greatly and reduce computing time
It is obvious that the interpolating ability is always far better than extrapolating ability. And
the nearer the data to training data, the better the extrapolation ability. So the model
perfomance can be improved greatly by enlarging the range of training data and changing
the model extrapolation issue into an model interpolation issue, which can be realized by
changing the fault free outside data into training data online. So it is advisable for a
modeling approach to be adaptive to improve the robustness of the FDD method.
In addition to extrapolating performance improvement, adaptive modeling also will
improve interpolating performance. The interpolating performance will be improved
further if some more nodes are added when the training data is very sparse and where it is
highly nonlinear, say, in the dry/wet area.
To realize the adaptability, modeling and FDD should be considered as a whole, because
fault free data should be guarantied in order to make use of the newly coming data. The
adaptive modeling scheme is shown as figure 5.1. After the model is trained using limited
original data, the model and the FDD system are commissioned. While the model and
FDD system are being commissioned, the measurements will be processed as follows:
Firstly, measurements will be feeded into a steady-state detector to obtain steady state data.
Secondly, steady state data are inputed to the preprocessor, where the model is embeded,
to decide whether the data are “ inside or near the training data” or not. If yes, the model
will generalize the measurements, and then feed the expected value to the classifier to
decide whether there is a fault or not, and if there is no fault, the new data will be recruited
into new training data and used to refine the original model . If the data are not “ inside or
near the training data”, the incoming data will not be processed further but will be stored.
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