Combining KPCA and LSSVM for HVAC fan machinery fault recognition

Abstract

In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input feature into a higher dimension feature space through a nonlinear map, the principal components are then found in the higher dimension feature space. Then the hyperparameters of LSSVM are optimized by particle swarm optimization. Then we compared the accuracies of the hybrid KPCA-LSSVM mode with other artificial intelligence (BPNN and fixed-SVM). The experimental results showed that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.