A Statistical, Rule-Based Fault Detection and Diagnostic Method for Vapor Compression Air Conditioners

Abstract

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.