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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Operation and maintenance optimization of offshore wind farms based on digital twin: A review
Xia, J., & Zou, G. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113322 · Cited by 112
Sun, S., Wang, T., & Chu, F. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112326 · Cited by 105
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66
“Other research using CNN techniques have explored detection of cracking in wind turbine blades [46], cracking on the surfaces of compressor blades in jet engines [9] and detection of surface erosion on turbine blades in jet engines [47].”
MethodAminzadeh, A., Dimitrova, M., Meiabadi, M. S., Karganroudi, S. S., Taheri, H., Ibrahim, H., & Wen, Y. (2023) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-023-00967-5 · Cited by 50
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
Ren, S., Hu, W., Bradbury, K., Harrison‐Atlas, D., Valeri, L. M., Murray, B. C., & Malof, J. M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119876 · Cited by 48
Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms
Altice, B., Dejesus, E. N., Davis, M., Shekaramiz, M., Moon, T. K., & Masoum, M. A. S. (2024) · Energies · doi:10.3390/en17050982 · Cited by 44
Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage
Song, X., Xing, Z., Jia, Y., Song, X., Cai, C., Zhang, Y., Wang, Z., Guo, J., & Li, Q. (2022) · Energies · doi:10.3390/en15207492 · Cited by 41
Ajibade, S.-S. M., Zaidi, A. T., Luhayb, A. S. M. A., Adediran, A. O., Voumik, L. C., & Rabbi, F. (2023) · International Journal of Energy Economics and Policy · doi:10.32479/ijeep.14832 · Cited by 41
Drone Footage Wind Turbine Surface Damage Detection
Foster, A., Best, O., Gianni, M., Khan, A., Collins, K. M., & Sharma, S. K. (2022) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmsp54334.2022.9816220 · Cited by 43
Advances in computer vision-based structural health monitoring techniques for wind turbine blades
Sheiati, S., & Chen, X. D. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116078 · Cited by 43
Visual attention prediction improves performance of autonomous drone racing agents
Pfeiffer, C., Wengeler, S., Loquercio, A., & Scaramuzza, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264471 · Cited by 38
Dubey, A. K., Kumar, A., Ramírez, I. S., & Márquez, F. P. G. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128926 · Cited by 29
Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model
Wu, Z., Zhang, Y., Wang, X., Li, H., Sun, Y., & Wang, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-74798-3 · Cited by 20
Terrestrial laser scanning for wind turbine blade defect detection
Stałowska, P., Suchocki, C., & Zagubień, A. (2025) · Measurement · doi:10.1016/j.measurement.2025.116706 · Cited by 15
A State-of-the-Art Review of Structural Health Monitoring Techniques for Wind Turbine Blades
Haseeb, S. A., & Krawczuk, M. (2025) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-025-01296-5 · Cited by 15
Rodriguez, A. A., Shekaramiz, M., & Masoum, M. A. S. (2024) · Drones · doi:10.3390/drones8040154 · Cited by 15
Masita, K., Hasan, A. N., & Shongwe, T. (2023) · Applied Sciences · doi:10.3390/app132413046 · Cited by 10
Xu, Y., Luo, X., Yuan, M., Huang, B., & Malof, J. M. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.1083005 · Cited by 9
Zhou, Y. C., Chen, J., Xu, R., Cao, Z., Jin, Z., Guo, Z., Wang, W., Wang, Z., & Li, K. (2022) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec55388.2022.10019897 · Cited by 8
Automatic Defect Detection in Wind Turbine Blade Images: Model Benchmarks and Re-Annotations
Gohar, I., See, J., Halimi, A., & Yew, W. K. (2023) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew59549.2023.00056 · Cited by 8
Detecting Small Damage on Wind Turbine Surfaces Using an Improved YOLO in Drone-Captured Scenes
Cao, J., Peng, B., Gao, M., Hao, H., Guo, J., Liu, X., & Liu, W. (2025) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-025-02130-w · Cited by 8
Damage Identification of Wind Turbine Blades - A Brief Review
Algolfat, A., Wang, W., & Albarbar, A. (2023) · Journal of Dynamics Monitoring and Diagnostics · doi:10.37965/jdmd.2023.422 · Cited by 8
Han, Y., Yu, F., He, Y., Liu, Z., Chen, Z., Wang, L., Zhang, F., Cao, Y. L., Zhang, X., & Shen, H. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2587783 · Cited by 3
Automated Wind Turbine Inspection and Structural Damage Detection Using YOLOv5s
Karpe, V., Gaikwad, R., Gole, O., Gore, P., Mirajkar, R., & Sawalkar, N. (2025) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi64286.2025.10984710 · Cited by 1
RGS-YOLO: A lightweight solution for surface defect detection in wind turbines
Sun, Z., & Wang, X. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343966 · Cited by 1
Comparison study of advanced computer vision models for wind turbine blade defect detection
Lateef, J., & Yu, X. (2026) · Wind Engineering · doi:10.1177/0309524x261428794 · Cited by 1
Improving Wind Turbines Blades Damage Detection by Using YOLO BoF and BoS
Mohammadi, R., & Sharifian, S. (2023) · doi:10.1109/icee59167.2023.10334850 · Cited by 1
Damage Identification of Wind Turbine Blades
de Oliveira, A. R. A., Medeiros, C. M. D. S., & Ramalho, G. L. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79035-5_13
Analysis of Small Wind Turbine Inspection via Autonomous Drone
Haymore, E., Guymon, K., Gotcher, I., Ahmadi, F., & Masoum, M. A. S. (2025) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc64455.2025.11039453
PAL, H. (2025) · Research Square · doi:10.21203/rs.3.rs-7377871/v1
Automated Structural Integrity Assessment of Wind Turbines Using YOLOv8
Jadhav, D. S., Gaikwad, R., Jagtap, A. M., & Kadam, V. (2025) · International Conference on Electronic Circuits and Signaling Technologies (ICECST) · doi:10.1109/icecst66106.2025.11307615
Zhao, Q., Zheng, Y., Zhang, Y., Cao, Y., Heng, Z., & Zhou, Y. (2026) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2606208
Tian, F., Wang, B., Li, J., Wang, Z., & Zhang, J. (2026) · Cluster Computing · doi:10.1007/s10586-026-06540-9
Thottempudi, S. G., Venkatesh, S., Moulla, D. K., Akinyemi, L. A., Agbo-Ajala, J. O., Ekundayo, O., Attipoe, D. S., Mnkandla, E., & Sumbwanyambe, M. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-08820-8_1
Li, Z., Yuan, Y., Meng, L., & Zhang, J. (2026) · Engineering Research Express · doi:10.1088/2631-8695/aea3a2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
