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|Author(s):||Daniel A. Veronica;|
|Title:||Detecting Cooling Coil Fouling Automatically-Part 2: Results Using a Multilayer Perceptron|
|Published:||September 01, 2010|
|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 ,queriedŠ 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|
|Pages:||pp. 599 - 615|
|Keywords:||automatic, fault detection, FDD, fouling, coil, heat exchanger, neural network, data-driven, machine learning,|
|Research Areas:||Fault Detection and Diagnostics, Cybernetic Building Systems, Building Automation and Control|
|PDF version:||Click here to retrieve PDF version of paper (7MB)|