2results about How to "Improve monotonicity" patented technology

The invention discloses a method for predicting the residual life of a wind power rolling bearing based on transfer learning. The method comprises the following steps: firstly, processing a vibration acceleration signal by using an envelope demodulation algorithm, and screening demodulation characteristics with good monotonicity to construct a bearing health index; thirdly, through a minimum distance sample pairing algorithm, pairing a target domain degradation sample with a source domain full-life sample; and then, constructing a deep convolutional migration neural network, and learning feature extraction and mapping by using paired samples to realize bearing health state estimation. And finally, fitting a state estimation result and combining a failure threshold value to predict the residual life. The method has the beneficial effects that an effective health index construction method is provided, an accurate sample pairing algorithm is designed, a high-performance state estimation model is established, and the prediction precision of the residual life of the wind power bearing is remarkably improved.

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.