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WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term memory network (CSwLSTM) model based on one-dimensional convolutional neural network (1D-CNN) and the shared weight long short-term memory network (SWLSTM) was proposed for wind turbine rolling bearing fault diagnosis. Using the Western Reserve University rolling bearing dataset for experiment, compared with the convolutional long short-term memory network (CLSTM) model and convolutional gated recurrent unit network (CGRU) model with the same structure, CSWLSTM model had a significant improvement in the convergence of the training dataset. The training time was reduced by 39.9% and 19.0%, respectively. The model parameters were reduced by 63. 3% and 53.4%, respectively. The accuracy was increased by 1.0% and 1.5%, the precision rate was increased by 1.0% and 1.7%, and the recall rate was increased by 0.9% and 1.0% on the test dataset, respectively. The simulation experiment results show that the CSWLSTM model has good application potential in the wind turbine rolling bearing fault diagnosis.
WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term memory network (CSwLSTM) model based on one-dimensional convolutional neural network (1D-CNN) and the shared weight long short-term memory network (SWLSTM) was proposed for wind turbine rolling bearing fault diagnosis. Using the Western Reserve University rolling bearing dataset for experiment, compared with the convolutional long short-term memory network (CLSTM) model and convolutional gated recurrent unit network (CGRU) model with the same structure, CSWLSTM model had a significant improvement in the convergence of the training dataset. The training time was reduced by 39.9% and 19.0%, respectively. The model parameters were reduced by 63. 3% and 53.4%, respectively. The accuracy was increased by 1.0% and 1.5%, the precision rate was increased by 1.0% and 1.7%, and the recall rate was increased by 0.9% and 1.0% on the test dataset, respectively. The simulation experiment results show that the CSWLSTM model has good application potential in the wind turbine rolling bearing fault diagnosis.
WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
JING DongXing (author) / CHEN YangHui (author) / QUAN Zhe (author)
2023
Article (Journal)
Electronic Resource
Unknown
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