Application of Optimized Directed Acyclic Graph support vector machine based on complex network in fault diagnosis of rolling bearing

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (12) : 1-6.

SHI Rui-min YANG Zhao-jian

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Abstract

Due to the large quantities of crossed combinations of the fault patterns and evolution stages of rolling bearings, the general patterns recognition method was difficult to adapt to multivariate process. In view of the problem, an optimized directed acyclic graph support vector machine based on complex network was proposed. According to the similarity measure in complex network theory, the separate 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 of distinguished degree of samples. Then the corresponding binary support vector machines were selected to construct the optimal directed acyclic graph, which had achieved high correction identification ratio by alleviating error accumulation and improving fault tolerance of the upper nodes. Feature vectors were constructed from the crest factor, kurtosis coefficient and energy of product functions which were 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 could recognize the types and evolution grades effectively and has higher accurateness and productiveness than traditional multi-class support vector machines.

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complex network / DAG-SVM / rolling bearing / fault diagnosis

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SHI Rui-min YANG Zhao-jian. Application of Optimized Directed Acyclic Graph support vector machine based on complex network in fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2015, 34(12): 1-6

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