Peer-reviewed articles 17,970 +



Title: IMPROVED DEEP LEARNING APPROACH IN WIND TURBINE DAMAGE DETECTION

IMPROVED DEEP LEARNING APPROACH IN WIND TURBINE DAMAGE DETECTION
Pawel Knap; Patryk Balazy
10.5593/sgem2022V/4.2
1314-2704
English
22
4.2
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
The growing number of wind farms is creating an increased demand for their trouble-free operation. Damage to their components can be catastrophic. A particular component that can be subject to damage during long-term operation and is difficult to diagnose is the mechanical gearbox located in the turbine. Traditional approaches to the subject of damage detection require, in the final stage, the involvement of an expert. Therefore, the article proposes a method based on the Deep Learning solution. A transfer learning method and a pre-trained Inception V3 network were used. A gearbox with in three states of healthy, worn out but still working and damaged was analyzed. Signal spectrograms were created from accelerometric measurements and then used as input for the neural network. Various approaches to creating spectrogram images were tested. The InceptionV3 network was taught on images generated in grayscale, and RGB and HSV. Channel reduction in the form of using grayscale improved the speed of the algorithm at the expense of precision. The use of HSV scale, on the other hand, made it more precise in detecting a worn out state.
[1] Polinder, H., Ferreira, J. A., Jensen, B. B., Abrahamsen, A. B., Atallah, K., & McMahon, R. A. (2013). Trends in wind turbine generator systems. IEEE Journal of emerging and selected topics in power electronics, 1(3), 174-185.
[2] Dupuis, Richard. "Application of oil debris monitoring for wind turbine gearbox prognostics and health management." Annual Conference of the PHM Society. Vol. 2. No. 1. 2010.
[3] Li, D., Ho, S. C. M., Song, G., Ren, L., & Li, H. (2015). A review of damage detection methods for wind turbine blades. Smart Materials and Structures, 24(3), 033001.
[4] Oliveira, G., Magalhaes, F., Cunha, A., & Caetano, E. (2018). Vibration-based damage detection in a wind turbine using 1 year of data. Structural Control and Health Monitoring, 25
[5] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
[6] P. Balazy, P. Gut, and P. Knap, “Convolutional mask-wearing recognition algorithm for an interactive smart biometric platform,” Robotic Systems and Applications, vol. 1, no. 2, pp. 35–40, Sep. 2021, doi: 10.21595/rsa.2021.22108.
This project was possible due to the second edition of the IDUB/2022/3870 grant support, which was created to support Polish students’ research groups in participation in international events. This project has been developed with New-Tech students’ research group located in Faculty of Mechanical Engineering and Robotic, AGH University of Science and Technology in Krakow, Poland
conference
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 06-08 December, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
547-552
06-08 December, 2022
website
8884
wind turbine, damage detection, deep learning, green energy