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

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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 remaining useful life (RUL) prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and Bayesian optimization of self-attention mechanisms for bidirectional long short-term memory (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. Finally, experimental validation is carried out based on the PRONOSTIA platform dataset and the XJTU-SY rolling bearing dataset, and the results show that the method proposed in this paper is better than many mainstream life prediction methods for rolling bearings at present, and the prediction accuracy is higher.