Application of optimized directed acyclic graph support vector machine based on complex network in fault diagnosis of rolling bearing

Due to the large amount of crossed combinations of fault patterns and evolution stages of rolling bearings, the general patterns recognition method is difficult to adapt to multivariate process. In view of the problem, an optimized directed acyclic graph support vector machine (DAG-SVM) based on complex network (CN) was proposed. According to the similarity measure in complex network theory, the separating characters of samples were evaluated, and the nodes of directed acyclic graph were sequenced by the average similarity measure which was calculated as the criterion for distinguishing degree of samples. Then the corresponding binary support vector machines were selected to construct an optimal directed acyclic graph, to achieve high correction identification ratio by alleviating error accumulation and improving fault tolerance of the upper nodes. Feature vectors were constructed of the crest factor, kurtosis coefficient and energy of product functions, obtained by local mean decomposition. And then the feature vectors were served as input parameters of CNDAG-SVM classifier to sort fault patterns and evolution stages of rolling bearings. By analyzing the vibration signal acquired from the bearings with inner-race, outer-race or elements faults, the experimental results indicate that the proposed method can recognize the fault types and evolution grades effectively and has higher accuracy and productiveness than traditional multi-class support vector machines. ©, 2015, Chinese Vibration Engineering Society. All right reserved.