Fault Detection of Wind Turbine Blades Using Multi-Channel CNN

Meng-Hui Wang, Shiue- Der Lu, Cheng-Che Hsieh and Chun-Chun Hung
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Meng-Hui Wang: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Shiue- Der Lu: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Cheng-Che Hsieh: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Chun-Chun Hung: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan

Sustainability, 2022, vol. 14, issue 3, 1-17

Abstract: This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades.

Keywords: multi-channel convolutional neural network; wind turbine; fault detection; triaxial vibration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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