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An Experimental Study on Multi-Fault Detection and Diagnosis Analysis of HVAC System  

Cho Sung-Hwan (Building Energy Research Center, KIER)
Hong Young-Ju (Graduate School of Mechanical Design Engineering, ChungNam National University)
Yang Hooncheul (Building Energy Research Center, KIER)
Ahn Byung-Cheon (Department of Building Equipment System Engineering, Kyungwon University)
Publication Information
Korean Journal of Air-Conditioning and Refrigeration Engineering / v.16, no.10, 2004 , pp. 932-941 More about this Journal
Abstract
The objective of this study is to detect the multi-fault of HVAC system using a new pattern classification technique. To classify the effect of single-fault in determining the pattern, supply air temperature, OA-damper, supply fan, and air flowrate were chosen as experimental parameters. The combination of supply temperature, flow rate, supply fan and OA-damper were chosen as multi-fault conditions. Three kinds of patterns were introduced in the analysis of multi-fault problem. To solve multi-fault problem, the new pattern classification technique using residual ratio analysis was introduced to detect the multi-fault as well as single-fault. The residual ratio could diagnose single-fault or multi-fault into several patterns.
Keywords
Fault detection and diagnosis; Variable air volume; Neural network; HVAC system; Multi-fault;
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