Fault Condition Recognition of mine hoist Combining Kernel PCA and SVM

Abstract

In this paper, a novel fault condition recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. Based on the analyses of kernel principal component analysis and support vector machine, the process of method is presented. KPCA firstly maps the original inputs into a high-dimensional feature space by a non-linear mapping, and then calculates principal component as input feature vectors of classifier of SVM, finally the results of fault condition recognition are calculated by SVM classification. Experiment using the real monitoring data sets shows the proposed method can afford credible fault condition detection and recognition.