Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN





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


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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Zhou Z. Review of fault diagnosis technology of fan gearbox [J]. Technology and Market, 2016,23 (04): 25-26+28.

Ma Weiwei. Current situation and development trend of international wind power industry [J]. Enterprise Reform and Management, 2019 (15): 209-213.

Qiao L. Current situation and prospect of global wind power development [J]. Wind Energy, 2012 (09): 58-62.

Xia Yunfeng. Inventory of China's wind power policy in 2019 [J]. Wind Energy, 2020 (01): 64-70.

Li Haozhang, Liu Pingyuan, Wang Jinhong, et al. Analysis of the present situation and future prospect of China's wind power industry [J]. Electromechanical Information, 2020 (21): 91-94.

Ji Z. Research on the present situation and development trend of wind power industry in China [J]. china plant engineering, 2020 (18): 217-218.

Qin Haiyan. The wind power industry will achieve high-quality development in 2018 [J]. Wind Energy, 2018 (12): 1-1.

Information Office of the State Council of the People's Republic of China. China's energy development in the new era [N]. People's Daily, 2020-12-22(010).

Miao He, David He. Deep Learning Based Approach for Bearing Fault Diagnosis[J]. IEEE Transactions on Industry Applications, 2017, 53(3): 3057-3065. DOI: https://doi.org/10.1109/TIA.2017.2661250

Fu C. Research on fault diagnosis method of fan gearbox based on deep learning [D]. Jiangnan University, 2020.

Wang Qingzhao, Wang Mingjun, Zhu Bin. Analysis of major accidents of wind turbines (I) [J]. Wind Energy, 2014 (06): 60-63.

Zhang P, Lu D. A survey of condition monitoring and fault diagnosis toward integrated O&M for wind turbines[J]. Energies, 2019, 12(14): 2801. DOI: https://doi.org/10.3390/en12142801

de Azevedo H D M, Araújo A M,Bouchonneau N. A review of wind turbine bearing condition monitoring:State of the art and challenges[J]. Renewable and Sustainable Energy Reviews, 2016, 56: 368-379. DOI: https://doi.org/10.1016/j.rser.2015.11.032

Chen X, Guo Y, Xu C, et al. Review of research on fault diagnosis and health monitoring of wind power equipment [J]. China Mechanical Engineering, 2020,31 (02): 175-189.

Li H, Xiao D. Overview of data-driven fault diagnosis methods [J]. Control and Decision, 2011,26 (1): 1-9+16.

Menezes E J N, Araújo A M, da Silva N S B. A review on wind turbine control and its associated methods[J]. Journal of cleaner production, 2018, 174(6): 945-953. DOI: https://doi.org/10.1016/j.jclepro.2017.10.297

CHEN J, LIU Z, WANG H, et al. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 67(2): 257-269. DOI: https://doi.org/10.1109/TIM.2017.2775345

Zhang Wei, Bai Kai, Song Peng, et al. Fault feature extraction method of fan rolling bearing based on VMD and singular value energy difference spectrum [J]. North China Electric Power Technology, 2017 (3): 59-64.

ZHAO S, YIN L, ZHANG J, et al. Real‐time fabric defect detection based on multi‐scale convolutional neural network[J]. IET Collaborative Intelligent Manufacturing, 2020, 2(4): 189-196. DOI: https://doi.org/10.1049/iet-cim.2020.0062

REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE, 2016:779-788. DOI: https://doi.org/10.1109/CVPR.2016.91




How to Cite

Meng G, An Y, Zhang D, Li X. Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 5 [cited 2024 May 20];11. Available from: https://publications.eai.eu/index.php/ew/article/view/5652