Ensemble learning-based intelligent fault diagnosis method using feature partitioning

Diversity and fusion strategy are the key factors which affect the performance of the ensemble learning systems. In this paper, to tackle the neighborhood factor selecting difficulty of the traditional neighborhood rough set method, the wrapper feature selection algorithm based on kernel neighborhood rough set is introduced to find a set of feature subsets with high diversity, and then a base classifier selection method is proposed for constructing the ensemble learning systems. To increase the diversity, the heterogeneous ensemble learning algorithm based on the proposed base classifier selection method is designed and compared with the similar homogeneous ensemble learning algorithm. To study the effect of the fusion strategy on the final performance of the ensemble learning system, majority voting and D-S theory for fusing the outputs of base classifiers of ensemble learning system to get final decision are compared experimentally. The results on UCI data and the fault signals of rotor-bearing system show that the heterogeneous ensemble learning system with D-S fusion strategy can get the best classifying performance, and the ensemble learning system is superior to single classification system in most cases