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Bearing life prediction method based on improved polarization self-attention optimization

PendingCN122287328AImprove the ability to extract key degradation informationImprove capture abilityFeature extractionEngineering
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.
Owner:YANSHAN UNIV