journal article·2024Open access

Evolutionary Variational YOLOv8 Network for Fault Detection in Wind Turbines

Hongjiang Wang, Qingze Shen, Qin Dai, Yingcai Gao, Jing Gao, Tian Zhang

Computers, Materials & Continua· Volume 80· Issue 1

Abstract

Deep learning has emerged in many practical applications, such as image classification, fault diagnosis, and object detection. More recently, convolutional neural networks (CNNs), representative models of deep learning, have ... | Find, read and cite all the research you need on Tech Science Press
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Research context

OpenAlex topics: Machine Fault Diagnosis Techniques · Fault Detection and Control Systems · Industrial Vision Systems and Defect Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (44)

Show source-provided references
1. Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method10.1016/j.renene.2019.06.094 ↗
2. Deep Learning for fault detection in wind turbines10.1016/j.rser.2018.09.012 ↗
3. A study of supervised machine learning techniques for structural health monitoring
4. Defect detection on a wind turbine blade based on digital image processing10.3390/pr9081452 ↗
5. Acoustic emission evaluation of composite wind turbine blades during fatigue testing
6. Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
7. Deep learning for computer vision: A brief review
8. MESON: A mobility-aware dependent task offloading scheme for urban vehicular edge computing10.1109/TMC.2023.3289611 ↗
9. You only look once: Unified, real-time object detection
10. ultralytics/yolov5: v5.0–YOLOv5-P6 1280 models, AWS, Supervisely and YouTube integrations
11. A comprehensive survey of neural architecture search: Challenges and solutions
12. B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv preprint arXiv:1611.01578, 2016.
13. H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055, 2018.
14. A survey on evolutionary neural architecture search10.1109/TNNLS.2021.3100554 ↗
15. Rich feature hierarchies for accurate object detection and semantic segmentation
16. Fast r-cnn
17. Damage identification of wind turbine blades with deep convolutional neural networks10.1016/j.renene.2021.04.040 ↗
18. Temporal attention convolutional neural network for estimation of icing probability on wind turbine blades10.1109/TIE.2021.3090702 ↗
19. YOLO9000: Better, faster, stronger
20. J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
21. A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
22. C. Li et al., “YOLOv6: A single-stage object detection framework for industrial applications,” arXiv preprint arXiv:2209.02976, 2022.
23. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
24. DC-YOLOv8: Small-size object detection algorithm based on camera sensor10.3390/electronics12102323 ↗
25. WT-YOLOX: An efficient detection algorithm for wind turbine blade damage based on YOLOX10.3390/en16093776 ↗
26. SOD-YOLO: A small target defect detection algorithm for wind turbine blades based on improved YOLOv510.1002/adts.202100631 ↗
27. Pareto-wise ranking classifier for multi-objective evolutionary neural architecture search
28. Learning to optimize: Reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system10.1109/TCYB.2021.3086501 ↗
29. Neural architecture search based on a multi-objective evolutionary algorithm with probability stack10.1109/TEVC.2023.3252612 ↗
30. AS-NAS: Adaptive scalable neural architecture search with reinforced evolutionary algorithm for deep learning10.1109/TEVC.2021.3061466 ↗
31. Learning transferable architectures for scalable image recognition
32. Neural predictor for neural architecture search
33. ReNAS: Relativistic evaluation of neural architecture search
34. Single-domain generalized predictor for neural architecture search system10.1109/TC.2024.3365949 ↗
35. Genetic programming for ensemble learning in face recognition
36. Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: A survey-part III10.1504/IJBIC.2021.116578 ↗
37. A novel fuzzy neural network architecture search framework for defect recognition with uncertainties10.1109/TFUZZ.2024.3373792 ↗
38. Adaptive swarm intelligent offloading based on digital twin-assisted prediction in VEC
39. A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization10.1080/0305215X.2014.941532 ↗
40. Evolutionary computing methodology for small wind turbine supporting structures10.1007/s00170-018-2860-6 ↗
41. Run, don’t walk: Chasing higher FLOPS for faster neural networks
42. Enhancing geometric factors in model learning and inference for object detection and instance segmentation10.1109/TCYB.2021.3095305 ↗
43. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
44. Continuous ant-based neural topology search