Local-global cooperative least squares support vector machine and prediction of remaining useful life of rolling bearing

Abstract

Aiming at the inability to accurately predict the remaining useful life of rolling bearings due to the phased degradation in the bearing degradation process, this paper proposes a local-global cooperative learning strategy to solve the problem that the information cannot be fully utilized due to the characteristics of the data. And the strategy is combined with the least squares support vector machine to build a regression model, which improves the accuracy of the remaining useful life of bearings. The strategy evaluates the health state of bearing’s degradation process based on singular value decomposition and kurtosis criteria to divide each degradation stage of the bearing so that the degradation information of each stage of the bearing can be learned. Then, according to the proposed cooperative learning mechanism, the local learning of each stage of the bearing is extended to the global learning of the total degradation process of the bearing. Finally, the learning strategy is applied to the least squares support vector machine model to predict the remaining useful life of bearings better. The results on the PHM2012 dataset show that our method’s values of root mean square error and mean absolute percentage error are 35.4461 and 0.2041, respectively.