The Time Element in Chiller Fault Detection and Diagnosis

Abstract

Vapor compression chillers are the central component of most large commercial air conditioning systems. Process supervision including fault detection and diagnosis (FDD) has been proposed as a means to identify unacceptable performance of chillers. FDD methodologies are often based on the comparison of the actual performance to some expected performance. An installation provided the case study data presented in this thesis which revealed a periodic variation in the chilled water flow rate and several other chiller monitored quantities. Time series analysis techniques were applied and shown to be capable of quantifying the time variation, resulting in increased fault detection and diagnosis capabilities.