This invention provides a bearing life prediction method based on improved polarization self-attention optimization, belonging to the field of
feature extraction and prediction. The method includes the following steps: S1, constructing a gated recurrent
unit model based on a polarization self-attention mechanism; S2, constructing a gated recurrent
unit model optimized based on a time-related polarization self-attention mechanism; S3, optimizing and training the parameters of the gated recurrent
unit model, and then testing the model by inputting the
test set data of the bearing dataset into the trained gated recurrent unit model for deep
feature extraction; S4, based on the deep
feature extraction, constructing a deep interactive model to achieve
accurate estimation and
uncertainty quantification of the bearing's
remaining life. The method of this invention can extract deep degradation features from long-term
series data and enhance the ability to capture early information of the sequence by introducing a time weighting factor, thus accurately predicting the bearing's
remaining life.