This paper presents a new fault detection and diagnosis (FDD) strategy for centrifugal chillers in building air-conditioning systems. The strategy is implemented using the fuzzy modeling and the artificial neural network techniques. Based on the sensitivity analysis of performance indices (PIs) to the chiller faults, the PI residuals between the model estimations using the normal data and the model estimations using the fault data are chosen to be normalized and to form a diagnostic classifier. The chosen PI risiduals are further quantified by the proposed fuzzy models. The fault identification is realized by neurasl network, using the quantitative diagnostic classifier. The use of the quantitative diagnostic classifier in this FDD strategy overcomes the problem faced by classical qualitative fault classifiers when different faults have the same linguistic rule pattern. The strategy implementation is based on the data at normal and faulty operating conditions at a range of load levels and fault severity levels. The strategy is validated using the laboratory chiller data provided by ASHRAE Research Project RP-1043 (Comstock and Braun 1999c).
ASJC Scopus subject areas
- Building and Construction