Gearbox fault diagnosis based on R-vine Copula-DBN

Low diagnostic accuracy results from the wide set of directed acyclic graphs that must be searched when doing structure learning on dynamic Bayesian starting networks under multidimensional input. Conventional approaches find it challenging to find the best structure. In this paper, a method is proposed to combine the R-vine Copula model with a dynamic Bayesian network (DBN) for fault diagnosis. First, the network structure space is made smaller by using the structure prediction model to filter the retrieved features and identify nodes with high correlation. Then, the first-layer tree structure of the R-vine Copula model is used combined with the transfer entropy method to construct the initial network of dynamic Bayesian network, and the DBN of the initial network is built according to the Markov process in time series for fault diagnosis, which solves the problem that it is difficult to obtain the optimal structure in the network construction under multiple features. The gearbox data of Southeast University is used for verification, and the comparison results show that the method can better learn the DBN structure, and the fit between the data and the model is high, and good diagnostic results can be obtained in fault diagnosis.