한동원 장영수 김서영 김용찬 2024-01-20T18:31:57Z 2024-01-20T18:31:57Z 2021-09-06 2010-10 1229-6422 https://pubs.kist.re.kr/handle/201004/131041 In this study, FDD algorithm was developed using the normalized distance method and general pattern classifier method that can be applied to constant air volume air handling unit(CAV AHU) system. The simulation model using TRNSYS and EES was developed in order to obtain characteristic data of CAV AHU system under the normal and the faulty operation. Sensitivity analysis of fault detection was carried out with respect to fault progress. When differential pressure of mixed air filter increased by more than about 105 pascal, FDD algorithm was able to detect the fault. The return air temperature is very important measurement parameter controlling cooling capacity. Therefore, it is important to detect measurement error of the return air temperature. Measurement error of the return air temperature sensor can be detected at below 1.2℃ by FDD algorithm. FDD algorithm developed in this study was found to indicate each failure modes accurately. Korean 대한설비공학회 정풍량 공조시스템의 고장검출 및 진단 시뮬레이션 Fault Detection and Diagnosis Simulation for CAV AHU System Article 2 설비공학 논문집, v.22, no.10, pp.687 - 696 설비공학 논문집 22 10 687 696 kci ART001488414 Fault detection and diagnosis(고장검출 및 진단) HVAC equipment(공조설비) Normalized distance method(표준화 거리 기법) Classifier(분류기)