Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM

Abstract

Rolling element bearings are critical components in rotating machinery. To tackle the problem of difficult to accurately characterize the operating state of rolling bearings caused by irrelevance and varying sensitivity of multiple features to performance degradation, and introduction of subjective errors in determination of hyperparameters of deep learning models, which can affect the accuracy and efficiency of RUL prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and BSBiLSTM. Firstly, Multi-domain features are extracted from noise-reduced vibration signals. Then, a three-criterion constraint-based feature selection algorithm is used and a secondary selection algorithm with Pearson correlation coefficient is proposed to improve data quality. Next, the 3σ criterion is integrated to determine the first prediction time for rolling bearings and to divide the degradation stage. Subsequently, the BiLSTM model with Bayesian optimization and self-attention mechanism is proposed to predict the RUL of rolling bearings to further improve the algorithm efficiency. Experimental results on PRONOSTIA platform dataset demonstrate that the proposed method outperforms many existing mainstream RUL prediction techniques, exhibiting superior prediction accuracy.