A roller bearing fault diagnosis method using interval support vector deterministic optimization based on nested PSO

Abstract

An interval support vector deterministic optimization model (ISVD) is proposed for the fault classification problem of uncertainty samples in this paper. Firstly, based on the order relation theory, support vector machines using interval samples is transformed into ISVD, which is a two-objective optimization problem, with the midpoint and the radius optimized at the same time. Then, ISVD is converted into a single objective optimization model by the linear combination. The single objective optimization model includes Lagrange multiplier vector and interval sample vectors, both of which are nested. Thus, the nested particle swarm optimization (PSO) based on dynamic decreasing inertia weight is applied to select the optimal Lagrange multiplier vector of this model. Lastly, the effectiveness of the proposed method is proved by the data set of University of California Irvine (UCI) and the roller bearing fault experiments. The experimental results show: ISVD owns outstanding generalization ability with the help of the structured risk minimization and global optimization. The accuracy of the proposed ISVD is better than that of the native Bayes uncertain classification method 1 (NBU1), native Bayes uncertain classification method 2 (NBU2) and the formula-based Bayes classifier (FBC).