Full
Outline
Addressing the limitations of traditional feedforward neural networks and convolutional neural networks in effectively extracting nonlinear spatial and temporal features from wind turbine operational data, and the current wind turbine fault diagnosis methods that can only perform state monitoring without effective fault localization, this paper proposes a wind turbine fault diagnosis method based on Chebyshev Graph Convolutional Networks and Gated Recurrent Units. First, a graph structure is constructed based on Dynamic Time Warping algorithm; second, Chebyshev Graph Convolutional Networks are used to extract nonlinear spatial correlations from wind turbine operational data; then, Gated Recurrent Units are employed to extract temporal features from the operational data; finally, the wind turbine fault status and fault location are output through fully connected layers and Softmax activation function. Experimental validation shows that this method can not only diagnose potential faults in wind turbines but also effectively determine the specific components where faults occur, achieving an accuracy of 99.33%, with a low false alarm rate of 0.38% and a low missed detection rate of 0.41%.
108
34
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
