Classification of fault location and performance degradation of a roller bearing

Effective fault location classification and especially performance degradation assessment of a roller bearing have been the subject extensive research, which can reduce costs and the nonscheduled down time. In this paper, a new fault diagnosis method based on multiple features, kernel principal component analysis (KPCA) and particle swarm optimization-support vector machine (PSO-SVM) is put forward. First, traditional features of the vibration signals in time-domain and frequency-domain are calculated, and then two types of features referred to as singular values and AR model parameters based on ensemble empirical mode decomposition (EEMD) are introduced. After that, the original feature vectors are mapped into higher dimensional space and the kernel principal components are extracted as new feature vectors, which are used as inputs to PSO-SVM. The experimental results show that the new diagnosis approach proposed in this paper can identify not only the fault locations but also the performance degradation of the roller bearing.

► EEMD signal processing method can overcome the mode mixing problem of EMD. ► We propose two types of features based on EEMD. ► We use KPCA to feature extraction and eliminate redundancy. ► We use PSO-SVM for the intelligent pattern classification. ► We compare the identification ability of several methods.