Abstract: Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is of paramount importance for ensuring the safe, stable, and reliable operation of engineering equipment. Existing deep learning prediction methods often directly establish a mapping relationship between vibration monitoring data and RUL, typically overlooking the differential states of rolling bearing performance degradation and neglecting the diversity of features extracted by deep learning models, leading to significant bias in RUL prediction results. In light of this, a novel method for dividing the degradation state of rolling bearings and predicting RUL is proposed. Features of bearing vibration signals are extracted, and the Mann Kendall test is employed to judge trends, determining the starting point of the degradation period. The endpoint of the slow degradation period is identified through the trend of normalized singular value correlation coefficients. A rolling bearing RUL prediction model based on a bidirectional long short term memory network with attention (Bi-LSTM-Att) is constructed, and the slow degradation period data and corresponding RUL labels are used to train the prediction model to achieve RUL prediction. The accuracy and effectiveness of the proposed method for bearing RUL prediction are validated through a public bearing dataset.

Key words: rolling bearings, state division, bidirectional long short term memory network, attention mechanism, RUL prediction