Journal
APPLIED THERMAL ENGINEERING
Volume 107, Issue -, Pages 284-293Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2016.03.147
Keywords
Fault detection; Refrigerant charge fault; EWMA control charts; Principal component analysis; Variable refrigerant flow
Funding
- National Natural Science Foundation of China [51576074, 51328602]
- Beijing Key Lab of Heating, Gas Supply, Ventilating and Air Conditioning Engineering [NR2016K02]
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Degradation occurs in a VRF system after years of operation due to refrigerant leakage, mechanical failure or improper maintenance. VRF systems require approaches to detect faults and sustain its normal operation. This paper proposes a creative statistical method to detect the refrigerant charge faults in VRF systems, which is based on principal component analysis (PCA) and exponentially-weighted moving average (EWMA) control charts. The EWMA model is built with the residual vector of the PCA model. Data of the experimental VRF system is used to validate the advantages of the PCA-EWMA method. Results show that the combined use of PCA and EWMA methods can achieve better fault detection efficiency than PCA based T-2-statistic and Q-statistic methods at low fault severity levels. The robustness of the PCA-EWMA method in online fault detection is verified using the data from different type of VRF systems. (C) 2016 Published by Elsevier Ltd.
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