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Partial Fault Detection of an Air-conditioning System by using a Moving Average Neural Network  

Han, Do-Young (Department of Mechanical Engineering, Kookmin University)
Lee, Han-Hong (Kookmin University)
Publication Information
International Journal of Air-Conditioning and Refrigeration / v.11, no.3, 2003 , pp. 125-131 More about this Journal
The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.
Neural network algorithm; Fault detection system; Multi-type air-conditioning system; Energy conservation; Condenser fouling; Evaporator fan slowdown; Moving average method;
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