An enhanced fault detection method for centrifugal chillers using kernel density estimation based kernel entropy component analysis

Abstract

In building automation system, timely detecting the operational faults in water chillers is crucial to system operation management. As a linear data transformation technique, the performance of principal component analysis (PCA) based chiller fault detection method is limited, especially for the incipient ones, due to the nonlinearities in chillers. In addition, the control limit for the monitoring statistic, such as squared prediction error (SPE), is determined under the Gaussian assumption of the score variables, which can hardly be satisfied in water chillers. Therefore, an enhanced fault detection method with the application of kernel density estimation (KDE) and kernel entropy component analysis (KECA) algorithm is reported in this paper. Cauchy-Schwarz (CS) divergence was evaluated as monitoring statistic to measure the cosine of the angle between two data sets after being projected onto a dominated subspace, and then adopted as an index for dissimilarity. KDE with its bandwidth being optimized was also applied to estimate the distribution of the CS divergence, so that the control limit for fault monitoring could be determined. The proposed KDE based KECA-CS method was validated using the experimental data from ASHRAE RP-1043, and further compared to the PCA -SPE, kernel principal component analysis (KPCA)-SPE, and KECA-SPE methods. Results showed that the best performance could be realized when using the KDE based KECA-CS method. The reported fault detection ratio was over 68% for the seven typical chiller faults even at their corresponding least severity level. The average fault detection accuracy was over 90%.