This paper presents a method for automated detection and diagnosis of faults in vapor compression air conditioners that only requires temperature measurements, and one humidity measurement. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for both fault detection and diagnosis. For fault detection, statistical properties of the residuals for current and normal operation are used to classify the current operation as faulty or normal. A diagnosis is performed by comparing the directional change of each residual with a generic set of rules unique to each fault. This diagnostic technique does not require equipment-specific learning, is capable of detecting about a 5% loss of refrigerant, and can distinguish between refrigerant leaks, condenser fouling, evaporator fouling, liquid line restrictions, and compressor valve leakage.

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A technique for detecting refrigerant leaks by utilizing their impact on the thermodynamic states of the vapor compression cycle is described. Simulation and laboratory experiments were performed to determine which of 7 inexpensive measurements contribute significantly to detection confidence. Experimental results show that suction line superheat and liquid line subcooling are the minimum measurements needed to detect and isolate refrigerant leaks from the other faults considered and provides 99.9% detection confidence with 5.5% charge loss. The addition of a hot gas line temperature sensor improves performance by allowing a 4.3% loss of charge to be detected with the same confidence. Adding more measurements did not enable smaller leaks to be detected.

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