Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance

Abstract Aiming at the minority samples cannot be effectively diagnosed when the samples are limited and imbalanced, a multiple classifier ensemble of the weighted and balanced distribution adaptation method (MC-W-BDA) is presented to solve the rolling bearing’s fault diagnosis problem under the limited samples imbalance. We adopt random sampling to obtain enough different training sample sets whose base classifiers are trained in the Reproducing Kernel Hilbert Space. The appropriate base classifiers are integrated into strong classifiers by multiple classifier ensemble strategy to obtain the final result of classification. In addition, we propose A-distance method to automatically set the optimal parameter (balance factor) in MC-W-BDA. Experimental verification verifies the feasibility and effectiveness of proposed approach.