Scholarly record
IMPROVED DEEP LEARNING APPROACH IN WIND TURBINE DAMAGE DETECTION
Abstract
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.
Publication Impact Profile
- Citations
- Scopus - Citation Indexes: 3
- Captures
- Mendeley - Readers: 3
Publication details
References5
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. DOI: 10.1109/jestpe.2013.2280428
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. DOI: 10.36001/phmconf.2010.v2i1.1867
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. DOI: 10.1088/0964-1726/24/3/033001
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 DOI: 10.1002/stc.2238
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). DOI: 10.1109/cvpr.2016.308
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.