Fully Autonomous Inspection of Wind Turbine Blades Based on Drones and Artificial Intelligence
Authors: 
Minmin Ma, Hao Liu, Yuxin Gao, Shun Li, Chenzhe Ma, Daiming HuAuthors Info & Claims
Minmin Ma, Hao Liu, Yuxin Gao, Shun Li, Chenzhe Ma, Daiming HuAuthors Info & ClaimsArticle No.: 29, Pages 1 - 8
Abstract
The traditional manual inspection of fan blades is labor-intensive, time-consuming, and subjective, making it impossible to detect internal defects in the blades. Based on artificial intelligence technology and deep learning algorithm, the UAV carries edge computing terminals, high-definition cameras, thermal imagers and other equipment to study the UAV autonomous flight control algorithm and the dual light fusion multi type back mixed defect detection and positioning algorithm, and realizes the fast, efficient and intelligent detection of the internal and external defects of the fan blades in 15-30 minutes. The fully autonomous inspection of wind turbine blades based on drones and artificial intelligence can make up for the shortcomings of traditional methods, greatly improve inspection efficiency, and reduce potential safety accidents in wind farms.
References
[1]
Yu, J., He, Y., Zhang, F., Sun, G., Hou, Y., Liu, H., Li, J., Yang, R. and Wang, H. 2023. An IEEE Sensors Journal, 23, 8 (April 2023), 8727-8736.
[2]
Jiangtao Lv. 2022. Renewable energy promotes China's green development. China Economic Weekly, 19, 58-61.
[3]
The National Energy Administration releases the 2022 national electricity industry statistical data. Electric Power Survey and Design, 2023, (1): 1.
[4]
Zhu, Y., Liu, X., Li, S., Wan, Y. and Cai, Q. 2022. Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size. Machines, 10, 12 (December 2022), 1184. https://doi.org/10.3390/machines10121184.
[5]
Li, D., Ho, S.-C. M., Song, G., Ren, L. and Li, H. 2015. A review of damage detection methods for wind turbine blades. Smart Materials and Structures, 24, 3 (February 2015), 033001. DOI 10.1088/0964-1726/24/3/033001.
[6]
Yang, R., He, Y. and Zhang, H. 2016. Progress and trends in nondestructive testing and evaluation for wind turbine composite blade. Renewable and Sustainable Energy Reviews, 60 (July 2016), 1225-1250. https://doi.org/10.1016/j.rser.2016.02.026
[7]
Beattie, A. and Rumsey, M. Non-destructive evaluation of wind turbine blades using an infrared camera. American Institute of Aeronautics and Aeronautics, City, 1998
[8]
Chady, T., Sikora, R., Lopato, P., Psuj, G., Szymanik, B., Balasubramaniam, K. and Rajagopal, P. 2016. Wind turbine blades inspection techniques. Organ, 5 (May 2016), 16.
[9]
Yunze He, Xiang Li, Hongjin Wang, Review of full-cycle non-destructive testing of wind turbine blades based on visible light and thermal imaging. J.Mechanical Engineering, 1-14.
[10]
Chen Chen. Application research on unmanned aerial vehicle inspection technology for wind turbine blades in wind farms. Wind Power Generation, 2017, (5) 5.
[11]
Chenliang Fan, Jinyu Zhang, Huiqiang Zhang, Application of fully autonomous drone flight technology in wind turbine inspection. China Aviation, 2019, (18) 3.
[12]
Xiaonan Fang. Expanding Bo Intelligent: Leading wind turbine operation and maintenance with fully automated inspection. Robot Industry, 2020, (5) 5.
[13]
China Energy Corporation achieves good results in monitoring wind turbine blades using drones. Yunnan Electric Power Technology, 2015, 43(A01) 1.
[14]
Liong, S.-T., Gan, Y. S., Huang, Y.-C., Yuan, C.-A. and Chang, H.-C. 2019. Automatic defect segmentation on leather with deep learning. arXiv preprint arXiv:1903.12139 (March 2019). https://doi.org/10.48550/arXiv.1903.12139
[15]
Singh, J. and Shekhar, S. 2018. Road damage detection and classification in smartphone captured images using mask r-cnn. arXiv preprint arXiv:1811.04535 (November 2018). https://doi.org/10.48550/arXiv.1811.04535
[16]
Xiaofeng Jiao, Xingqun Jiang, Bo Liu, Wind turbine blade damage detection and recognition technology based on improved CenterNet algorithm. Inner Mongolia Electric Power Technology, 2022, (001) 040.
[17]
Ruxin Gao, Yongfei Ma, Xingguo Tan. Wind turbine blade surface damage detection based on unmanned aerial vehicle image analysis. Information and Computers, 2022, 34(20) 5.
[18]
Kun Chen. Research on wind turbine blade damage detection based on YOLO algorithm. Equipment Management and Maintenance, 2022, (016) 000.
[19]
Yongjun Qi, Hailin Tang, Zhenbang Huang. Research on automatic detection technology for wind turbine blade cracks based on multi-scale feature fusion. Jiangxi Science, 2022, (002) 040.
[20]
Jiawei Zhu, Chuanbo Wen. Wind turbine blade defect detection based on improved SSD. J. Composite Materials Science and Engineering, 2022, (003): 000.
[21]
Yuping Wu, Haixu Liu, Jingfang Wu, Application of improved YOLOv3-Tiny network in wind turbine blade damage detection. Hebei Industry and Technology, 2021.
[22]
Yanxia Liu, Xiaoyan Ding, Jianjun Fang. Wind power generator blade crack identification based on transfer learning. Sensors and Microsystems, 2019, (8) 4.
[23]
Junying Ying, Xun Liu, Xueren Zeng, Liang Fang, Nan Tian. Research and application of wind turbine blade defect detection based on optimized SSD algorithm. Zhejiang Electric Power, 2021, 040(008) 47-52.
[24]
Zhongkan Qu, Xuefeng Li, Fengjun Li, Design of wind turbine blade defect image multi-point detection system based on Faster R-CNN. Electronic Design Engineering, 2021, 029(004) 57-61.
[25]
Chao Zhang, Chuanbo Wen. Wind turbine blade defect detection based on improved Mask R-CNN. Renewable Energy, 2020, 38(9) 6.
[26]
Chenliang Fan, Guoqing Li, Changxiao Ma, Wind turbine blade crack detection algorithm based on deep learning. Science and Technology Innovation, 2020, (13) 4.
[27]
Luo, Q., Gao, B., Woo, W. L. and Yang, Y. 2019. Temporal and spatial deep learning network for infrared thermal defect detection. Ndt & E International, 108 (December 2019), 102164. https://doi.org/10.1016/j.ndteint.2019.102164
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Published In
December 2023
292 pages
ISBN:9798400709401
DOI:10.1145/3632314
Copyright © 2023 ACM.
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Published: 09 December 2023
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ISIA 2023
ISIA 2023: 2023 International Conference on Intelligent Sensing and Industrial Automation
December 9 - 10, 2023
Virtual Event, China
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