A robust online refrigerant charge fault diagnosis strategy for VRF systems based on virtual sensor technique and PCA-EWMA method. (5th June 2017)

• Record Type: Journal Article
• Authors: Liu, Jiangyan; Li, Guannan; Chen, Huanxin; Wang, Jiangyu; Guo, Yabin; Li, Jiong
• Is Part Of: Applied thermal engineering. Volume 119(2017)
• Page Start: 233
• Page End: 243
• Publication Date: 2017-06-05
• Subjects: Fault detection and diagnosis -- Refrigerant charge fault -- Variable refrigerant flow -- PCA-EWMA -- Virtual refrigerant charge sensor; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.03.074
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: VRC model can only identify VRF system charge faults at undercharge situation. PCA-EWMA method has shortcoming on detecting severe undercharge faults of VRF. A refrigerant charge fault diagnosis strategy is proposed based on VRC and PCA-EWMA. The proposed hybrid model showed high fault diagnosis accuracy and efficiency. Abstract: The enhancement of fault detection and diagnosis (FDD) strategy for air-conditioning system is always a complex difficulty. In previous studies, the virtual refrigerant charge (VRC) sensor method and principal component analysis (PCA) based exponentially-weighted moving average (EWMA) method were proposed to identify refrigerant charge faults for variable refrigerant flow (VRF) systems, respectively. However, both methods had defects in some cases. On the basis of complementary advantages, this study employs the VRC model to detect the undercharge faults as it shown outstanding efficiency on identifying undercharge cases. Similarity, the PCA-EWMA model is used to detect the overcharge faults, since it is very sensitive to the little variation in the overcharge situations. Further, a novel online refrigerant charge fault diagnosis strategy is proposed based on two fault detection methods, i.e. VRC method and PCA-EWMA method. The new hybrid model overcomes the defects of two previous methods appropriately and well inherits the advantages of both. Finally, the robustness of the proposed refrigerant charge fault diagnosis strategy is verified using the experimental data and online data collected from different type of VRF systems.