A novel performance trend prediction approach using ENBLS with GWO

Abstract Bearings are a core component of rotating machinery, and directly affect its reliability and operational efficiency. Effective evaluation of a bearing’s operational state is key to ensuring the safe operation of the equipment. In this paper, a novel prediction method of bearing performance trends based on the elastic net broad learning system (ENBLS) and the grey wolf optimization (GWO) algorithm is proposed. The proposed method combines the advantages of the ENBLS and GWO algorithms to achieve better prediction results. In order to solve the problem that traditional regression prediction algorithms may lead to unsatisfactory prediction results and long training time, we propose a performance trend prediction method based on ENBLS. To further improve the prediction accuracy, we utilize the GWO algorithm to optimize various parameters present in the model to improve the performance of the model. The bearing data of the whole life cycle from the 2012 IEEE PHM challenge are selected to verify the effectiveness of the proposed method. The results show that the proposed method has high prediction accuracy and stability.