Novel HVAC fan machinery fault diagnosis method based on KPCA and SVM

Abstract

In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. An integrated method is applied 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. The experimental results show that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.