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Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Author

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  • Wenxin Yu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
    School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Shoudao Huang

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Weihong Xiao

    (School of Information Engineering, Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Xiangtan University, Xiangtan 411105, China)

Abstract

To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability. Compared with Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other fault diagnosis methods, the average diagnostic correctness rate is higher; so, the method can provide more accurate reference information for wind turbine fault diagnosis.

Suggested Citation

  • Wenxin Yu & Shoudao Huang & Weihong Xiao, 2018. "Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System," Energies, MDPI, vol. 11(10), pages 1-11, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2561-:d:172061
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    References listed on IDEAS

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    2. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    3. P. Rinn & H. Heißelmann & M. Wächter & J. Peinke, 2013. "Stochastic method for in-situ damage analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(1), pages 1-5, January.
    4. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
    5. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
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    Cited by:

    1. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.

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