文章基本信息

标题：Fault Detection of Wind Turbine Blades Using Multi-Channel CNN
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作者：Meng-Hui Wang ; Shiue-Der Lu ; Cheng-Che Hsieh 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：3
页码：1781
DOI：10.3390/su14031781
语种：English
出版社：MDPI, Open Access Journal
摘要：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.