An effective fault diagnosis method for centrifugal chillers using associative classification

Abstract

Fault diagnosis for centrifugal chillers is very important for saving energy and maintaining optimal operating conditions. A fault diagnosis method for centrifugal chillers is proposed based on the associative classification (AC) algorithm, which constructs an associative classifier by excavating strong rules between fault classes and physical attributes. First, association rules with significant support and high confidence values are discovered. Instead of the Apriori algorithm, FP-growth is adopted to accelerate association rule mining. Second, only association rules named class association rules (CARs) whose consequents are limited to fault classes are preserved. Third, pruned CARs are obtained by means of ranking CARs and pruning the redundant rules according to the concept of “higher rank”. Fourth, a limited number of rules are selected out of pruned CARs based on the AC algorithm to construct an associative classifier. This approach is validated using experimental centrifugal chiller data from the ASHRAE Research Project 1043 (RP-1043). Results demonstrate that this proposed AC-based approach can effectively identify seven common chiller faults at both low and high severity levels and the average correct fault diagnosis ratio can be examined up to 86.3%.