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Article

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

1
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
3
School of Information Engineering, Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Xiangtan University, Xiangtan 411105, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(10), 2561; https://doi.org/10.3390/en11102561
Received: 28 August 2018 / Revised: 20 September 2018 / Accepted: 24 September 2018 / Published: 26 September 2018
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. View Full-Text
Keywords: spectrogram; convolutional neural network; wind turbine; fault diagnosis spectrogram; convolutional neural network; wind turbine; fault diagnosis
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MDPI and ACS Style

Yu, W.; Huang, S.; Xiao, W. Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System. Energies 2018, 11, 2561. https://doi.org/10.3390/en11102561

AMA Style

Yu W, Huang S, Xiao W. Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System. Energies. 2018; 11(10):2561. https://doi.org/10.3390/en11102561

Chicago/Turabian Style

Yu, Wenxin, Shoudao Huang, and 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 11, no. 10: 2561. https://doi.org/10.3390/en11102561

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