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Detecting Cooling Coil Fouling Automatically-Part 2: Results Using a Multilayer Perceptron

Published

Author(s)

Daniel A. Veronica

Abstract

The novel concept to detect cooling coil fouling automatically with an embedded data–driven agent using expert signature maps, introduced in a companion paper (Veronica 2010), is exercised here on data from computer simulations of clean and fouled cooling coils. The companion paper describes in detail a crucial element of its fouling detection concept: that responses from an adaptive, predictive data–driven model of the cooling coil sum to an appropriate value when the data–driven model is “queried” off–line from the real–time data acquisition. This query consists of a time–sequence of input vectors, passed through the model as a triangular pulse of water flow. This report shows rudimental success, a foundation for further research, is obtained using a relatively simple, three–node multilayered perceptron as the data–driven coil model. Also discussed here are factors critical to successfully employing a data–driven model of any architecture to detect coil fouling.
Citation
International Journal of Heating, Ventilating, Air-conditioning and Refrigerating Research
Volume
16
Issue
5

Keywords

automatic, fault detection, FDD, fouling, coil, heat exchanger, neural network, data-driven, machine learning

Citation

Veronica, D. (2010), Detecting Cooling Coil Fouling Automatically-Part 2: Results Using a Multilayer Perceptron, International Journal of Heating, Ventilating, Air-conditioning and Refrigerating Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=902772 (Accessed April 24, 2024)
Created September 1, 2010, Updated February 19, 2017