The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation
Mohamed Kandil J. J. McArthur
Gray-box models offer significant benefit over black-box approaches for
equipment emulator development for equipment since their integration of physics
provides more confidence in the model outside of the training domain. However,
challenges such as model nonlinearity, unmodeled dynamics, and local minima
introduce uncertainties into grey-box creation that contemporary approaches
have failed to overcome, leading to their under-performance compared with
black-box models. This paper seeks to address these uncertainties by injecting
noise into the training dataset. This noise injection enriches the dataset and
provides a measure of robustness against such uncertainties. A dynamic model
for a water-to-water heat exchanger has been used as a demonstration case for
this approach and tested using a pair of real devices with live data streaming.
Compared to the unprocessed signal data, the application of noise injection
resulted in a significant reduction in modeling error (root mean square error),
decreasing from 0.68 to 0.27{\deg}C. This improvement amounts to a 60%
enhancement when assessed on the training set, and improvements of 50% and 45%
when validated against the test and validation sets, respectively.