A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. (January 2017) - Liu, Zhiwen; Guo, Wei; Hu, Jinhai; Ma, Wensheng
Abstract: This paper proposes a hybrid intelligent method for multi-fault detection of rotating machinery, in which three methods, i.e. including the redundant second generation wavelet package transform (RSGWPT), the kernel principal component analysis (KPCA) and the twin support vector machine (TWSVM), are combined. Firstly, RSGWPT is used to extract feature vectors from representative statistical characteristics in the decomposition frequency band, and then the KPCA in the feature space is performed to reduce the dimension of features and to extract the dominant features for the following classification. Finally, a novel support vector machine, called twin support vector machine is used to construct a multi-class classifier. Inputting superior features to this classifier, the condition of the monitored machine component can be determined. Experimental results demonstrate that the proposed hybrid method is effective for multi-fault detection of rotating machinery. The TWSVM is also indicated that has better classification performance and faster convergence speed than the normal SVM. Highlights: Multi-fault classification based on hybrid RSGWPT-KPCA-TWSVM method is proposed. RSGWPT is employed to extract representative fault features. KPCA is used to reduce the dimension of features and to extract the useful features. TWSVM is introduced to implement the fault type classification. The experiments demonstrate high efficiency and robustness of the proposed method.