Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Guangyi Meng; Yuxing An; Dong Zhang; Xudong Li

doi:10.4108/ew.5652

Authors

Guangyi Meng Shenyang Institute of Engineering
Yuxing An Shenyang Institute of Engineering
Dong Zhang Shenyang Institute of Engineering
Xudong Li Shenyang Institute of Engineering

DOI:

https://doi.org/10.4108/ew.5652

Keywords:

Empirical mode decomposition method, Data expansion, Generative adversarial neural network, Fault diagnosis of wind turbine gearbox

Abstract

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper.

OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy.

METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis.

RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

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