A refrigerant charge fault detection method for variable refrigerant flow (VRF) air-conditioning systems

Abstract 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 T2-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.

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