A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
A large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism, ResNext network and long short-term memory (LSTM) network was proposed. Firstly, the collected one-dimensional vibration signal was preprocessed; then it was input into the model in two ways to extract features, and one of them was input into the ResNext module embedded in the attention mechanism. The attention mechanism can increase the weight of important features and reduce model operations. The other channel was input to the LSTM network to extract the dependence of the vibration signal on the time series. Finally, the two extracted features are fused and input to the Softmax layer for fault classification. The experimental results show that, compared with the current bearing fault diagnosis method based on deep learning, the proposed method performs better in bearing fault classification accuracy.
FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
A large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism, ResNext network and long short-term memory (LSTM) network was proposed. Firstly, the collected one-dimensional vibration signal was preprocessed; then it was input into the model in two ways to extract features, and one of them was input into the ResNext module embedded in the attention mechanism. The attention mechanism can increase the weight of important features and reduce model operations. The other channel was input to the LSTM network to extract the dependence of the vibration signal on the time series. Finally, the two extracted features are fused and input to the Softmax layer for fault classification. The experimental results show that, compared with the current bearing fault diagnosis method based on deep learning, the proposed method performs better in bearing fault classification accuracy.
FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
DU HaoFei (author) / ZHANG Chao (author) / LI JianJun (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
| DOAJ | 2023
Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing
| Wiley | 2020