Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults

Abstract

Developing fault detection and diagnosis (FDD) for the variable refrigerant flow (VRF) system is very important for saving energy and improving reliability of the equipment. For indoor unit (IDU) faults of VRF system, it is especially necessary to detect faults and identify which IDU is faulty. Therefore, this paper has proposed a fault detection strategy based on modularized PCA method, which cannot only detect faults but also specify the faulty IDU. Fault detection models are established respectively for outdoor unit (ODU) and IDUs of VRF system using the modularized PCA method. Then, the expert-based multivariate decoupling strategy with six variables for VRF system is developed to isolate faults. Four common faults are taken into account for VRF system, which include two IDUs faults (electronic expansion valve fault and IDU air-side fouling), one ODU fault (reversing valve stick) and one system fault (refrigerant undercharge). The proposed FDD strategy is evaluated by experimental data of four faults. The test results have shown that modularized PCA-based fault detection method and rule-based diagnosis method are effective for the four typical faults in VRF system. Therefore, it is quite suitable for FDD of VRF system.