Wind turbine bearing fault classification identification based on optimized variational mode decomposition and convolutional neural network-bidirectional gated recurrent unit-Attention.

As a key core component of wind turbine generators, the rolling bearings in the gearbox directly affect the overall performance and reliability of the wind turbine generators. Accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine generators. This paper takes the rolling bearing operation data as the research object and proposes a bearing fault classification research method based on the combination of variational mode decomposition (VMD) optimization and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU)-Attention model. Firstly, to address the sensitivity of intrinsic mode function (IMF) components in the VMD decomposition process, an improved RIEM algorithm is adopted to optimize the hyperparameters of the VMD algorithm. This process aims to adaptively adjust the penalty factor and decomposition layers of the VMD algorithm and find the optimal IMF component to determine the most suitable IMF component in the signal data. Secondly, to fully explore the complex characteristics of fault signals, composite multi-scale slope entropy is used to extract features from the optimized input data. By conducting multidimensional analysis on the local and global characteristics of the signal at different time scales, efficient representation of fault features is achieved. Finally, based on MATLAB, a simulation experiment platform is established. This paper conducts research on the classification of rolling bearing faults through the CNN-BiGRU-Attention model. The results show that the model established in this paper has significant effects and stable performance. The research in this paper provides new technical ideas for fault diagnosis of rolling bearings in wind turbine generator gear.