RUL prediction of rolling bearing based on MRSDAE-SOM combined with HGRU
Predicting the Residual Useful Life(RUL)of bearings based on traditional methods has many steps,is expensive,and the model is not generalizable.And the existing deep learning-based prediction methods often lead to overfitting of the model due to the excessive amount of data,which results in poor model accuracy.In order to overcome the above drawbacks,a rolling bearing RUL prediction method based on Manifold Regularization Stack Denoising Auto Encoder(MRSDAE)combined with Self-Organi-zing Mapping(SOM)combined with HGRU was proposed.Firstly,a bearing Health Indicator(HI)was constructed using an un-supervised network MRSDAE-SOM.Then,a prediction model was built through a Hierarchical Gated Recurrent Unit(HGRU)network.The HGRU network has the ability to capture temporal features and integrate attention information from differ-ent time scales by adding multi-scale and dense layers.Finally,experimental validation shows that compared with other data-driv-en methods,the proposed method constructs health indicator in an unsupervised way,which is efficient and easy to apply.The model generalizes well and effectively prevents overfitting,and achieves higher prediction accuracy.
Deep Learning(DL)Residual Useful Life(RUL)Manifold Regularized Stack Denoising Auto Encoder(MRSDAE)Hierarchical Gated Recurrent Unit(HGRU)
万方数据
维普
