Enhanced chiller faults detection and isolation method based on independent component analysis and k-nearest neighbors classifier

Abstract

The fault detection and isolation (FDI) of centrifugal chiller system is essential for improving energy efficiency and reducing energy consumption. Considering the non-Gaussianity of chiller measurement, this paper proposes an effective non-Gaussian chiller FDI method based on independent component analysis (ICA) and k-nearest neighbor (KNN) classifier. First of all, an enhanced fault detection method is developed by combining exponentially weighted moving average and ICA (EWICA). The exponentially weighted moving average is employed to develop a dynamic threshold scheme, which reduces the false alarm rate (FAR) and improves the fault detection rate (FDR) of chiller faults. Then, based on the KNN with cosine similarity metric, a novel fault isolation method using the direction of the residual vector is presented to isolate the chiller faults. To further improve the isolation performance, the number of neighbors (k) is optimized by 10-fold cross validation. Finally, the proposed FDI method is validated by using a data set from the ASHRAE Research Project 1043 (RP-1043). The FDI performance of the proposed method is comprehensively analyzed and compared with six state-of-the-art methods. Both of detection and isolation results suggest that the EWICA-based method delivers superior performance for chiller FDI.