A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems

Several faults affect heating, ventilation, and air conditioning (HVAC) chiller systems, leading to energy wastage, discomfort for the users, shorter equipment life, and system unreliability. Early detection of anomalies can prevent further deterioration of the chiller and energy wastage. In this work, a data-driven approach is used in order to detect faults that usually plague chiller systems. In particular, the proposed approach employs a kernel principal component analysis (KPCA) in order to capture the normal operative conditions of the system; the learning method turns out to be effective in handling nonlinear phenomena through the use of the Gaussian kernel, which, by means of a self-tuning procedure, ensures good accuracy properties while maintaining enough generalization characteristics. The effectiveness of the proposed fault detection method is evaluated by means of tests on emulated and real chiller data. The KPCA approach is first proved to exhibit better detection performances than linear PCA, and then, it is corroborated through the comparison with local outlier factor, one-class support vector machine, and isolation forest.