WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
JING DongXing
CHEN YangHui
QUAN Zhe
Abstract：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.
Keywords：Wind powerFault diagnosisRolling bearingShared weight long short-term memory networkOne-dimensional convolutional neural network
Publication Date：2023-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 1309-1317 )