| Title |
Fault Detection and Diagnosis Simulation for CAV AHU System |
| Authors |
한동원(Dong Won Han) ; 장영수(Young Soo Chang) ; 김서영(Seo Young Kim) ; 김용찬(Yongchan Kim) |
| Keywords |
고장검출 및 진단 ; 공조설비 ; 표준화 거리 기법 ; 분류기 Fault detection and diagnosis ; HVAC equipment ; Normalized distance method ; Classifier |
| Abstract |
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. |