Abstract
Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. To further improve the anti-noise ability of the model, the first layer of the model was set as the Drop-block layer. Finally, the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results. The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.
Highlights
Wind power is the fastest-growing renewable energy in the world
The vibration signals of rolling bearings are easy to be covered by strong interference signals in the industrial environment, which make it effective fault diagnosis difficult
This paper explores a new and improved anti-noise residual reduction network to solve fault diagnosis problem of wind turbine bearing under high noise environment
Summary
Wind power is the fastest-growing renewable energy in the world. With the development of the wind power industry, the fault diagnosis and maintenance of wind turbines have become more important [1,2]. Wang et al [16] explored a new sparse representation method that uses a new time-varying cosine-packet dictionary for the bearing fault diagnosis of wind turbines operating under varying speed condition that can adaptive to the variations of major frequencies of the vibration signals. A bearing fault diagnosis model based on an improved deep residual shrinkage network is proposed in this paper. This paper explores a new and improved anti-noise residual reduction network to solve fault diagnosis problem of wind turbine bearing under high noise environment. Some continuous signals are extracted from the original signal sequence to construct samples These sample are applied to training to improve the anti-noise residual shrinkage network. Samples not used in training are used to test the trained improved noise-resistant residual shrinkage network
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