The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A New Vision-Based Method Using Deep Learning for Damage Inspection in Wind Turbine Blades
Abstract
Wind turbines are having great impact in the field of clean energies. However, there is a need to improve these technologies in various aspects, such as: maintenance, energy storage, cases of overload or mechanical failure. In maintenance, they constantly suffer of damage in blades typically to be in the open air and in constant operation. The most well-known damages in blades are identified as: impact of rays, wearing, fractures by cutting forces, freezing, among others. Because of all these factors, it is necessary to develop a predictive technique to help us to do the inspections of the blades in a safer and more effective way than manual inspection. In that sense, this paper introduces a deep learning vision-based approach to automatically analyze each part of the face of the blade, capable of making the detection of certain faults (impact of rays, wear and fractures). In addition, we present a proof-of-concept using a robot to automatically detect failures in wind turbine blades. Experimental results validate our vision system.
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In the citing paper
“However, the training examples were of advanced damage that is clearly visible to the human eye.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [46]
In the citing paper
“All of these approaches use some form of feature extraction or segmentation techniques to normalise the input into a trained CNN and typically achieved a high accuracy of detection.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [46]
In the citing paper
“Additionally, all these applications required large quantities of training data.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [46]
Citations
Cites
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Towards better exploiting convolutional neural networks for remote sensing scene classification
Nogueira, K., Penatti, O. A. B., & dos Santos, J. A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.001 · Cited by 938
Wind power generation and wind turbine design
(2011) · Choice Reviews Online · doi:10.5860/choice.48-3904 · Cited by 292
Dao, P. B., Staszewski, W. J., Barszcz, T., & Uhl, T. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.06.089 · Cited by 222
Modelling and control of a wind turbine and farm
Hur, S. (2018) · Energy · doi:10.1016/j.energy.2018.05.071 · Cited by 58
Condition monitoring of wind turbine pitch controller: A maintenance approach
González, A., Cortadi, A. J., Galar, D. P., & Ciani, L. (2018) · Measurement · doi:10.1016/j.measurement.2018.01.047 · Cited by 45
Continuous Line Laser Thermography for Damage Imaging of Rotating Wind Turbine Blades
Hwang, S., An, Y., & Sohn, H. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.04.478 · Cited by 41
Autonomous and scalable control for remote inspection with multiple aerial vehicles
Clark, R., Punzo, G., MacLeod, C., Dobie, G., Summan, R., Bolton, G. T., Pierce, G., & Macdonald, M. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.10.012 · Cited by 22
Choices on the road to the clean energy future
Cash, D. W. (2017) · Energy Research & Social Science · doi:10.1016/j.erss.2017.10.035 · Cited by 20
Wind Turbine Blade Damage Detection Via 3-Dimensional Phase-Based Motion Estimation
Sarrafi, A., & Mao, Z. (2017) · International Workshop on Structural Health Monitoring (IWSHM) · doi:10.12783/shm2017/14154 · Cited by 15
Cited by
Sorted by citations
Damage detection techniques for wind turbine blades: A review
Du, Y., Zhou, S., Jing, X., Peng, Y., Wu, H., & Kwok, N. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106445 · Cited by 475
Mardanshahi, A., Sreekumar, A., Yang, X. P. O., Barman, S. K., & Chronopoulos, D. (2025) · Sensors · doi:10.3390/s25051424 · Cited by 189
Memari, M., Shakya, P., Shekaramiz, M., Seibi, A. C., & Masoum, M. A. S. (2024) · IEEE Access · doi:10.1109/access.2024.3371493 · Cited by 158
Chatterjee, J., & Dethlefs, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111051 · Cited by 156
Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades
Wang, W., Xue, Y., He, C., & Zhao, Y. (2022) · Energies · doi:10.3390/en15155672 · Cited by 136
Metadata
- Authors
- Moreno, S., Pena, M., Toledo, A., Trevino, R., & Ponce, H.
- Affiliations
- Universidad Panamericana (MX)
- Venue
- 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 1-5
- OpenAlex
- W2901225969
- Cited by
- 40
- References
- 10
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 2 · 2019: 1
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- wind turbine blades, damage detection, visual inspection, deep learning, fault detection, impact damage, blade wear, blade fracture, robotic inspection, predictive maintenance
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