Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems

This paper proposes a fault diagnosing system for the Ice-Storage Air-Conditioning System (ISACS) to supervise operation conditions of brine chillers. Combining Radial Basis Function Network (RBFN) and Robust Quality Design (RQD), an Enhanced RBFN (ERBFN) is proposed pursue fast accurate diagnosis. The RQD method used adjust parameters in training stage improve searching ability, good performance with close spike tracking capability can be seen. efficiency chiller ISACS was considered as quality characteristics, values measured by all instruments were control factors, noise factors abnormal variable system. ERBFN prevent equipment from being damaged without warning. diagnosis ensure normal. Experimental results are provided show effectiveness method. new artificial neural network algorithm this successfully applied ISACS. It not only provides reference enterprises but also studies on other topics future.