Wind energy, as the third largest power generation energy in China, has converted a lot of clean energy for our country, but because of its perennial exposure to the outdoor harsh environment, the fan blades will have different degrees of damage. The detection of blade defects plays a significant role in maintaining power generation efficiency and reducing accident rates. This paper proposes a multi-algorithm fusion method for blade defect detection based on edge computing platforms, aimed at enhancing real-time detection and accuracy through the low latency and high efficiency of edge computing technology. By analyzing the characteristics of edge computing technology and the multi-algorithm fusion mechanism, this paper designs a complete experimental procedure for blade defect detection, including sample collection, experimental equipment preparation, and test environment setup. In the experimental design, popular detection algorithms have been utilized and appropriately optimized for the edge computing environment to ensure the efficiency of data processing and analysis. The experimental results demonstrate that blade defect detection using the method proposed in this paper has significantly improved in terms of accuracy and response time. A comprehensive system performance evaluation and algorithm performance comparison indicate that the method proposed in this paper has high practical value and potential for widespread application in the field of blade defect detection in edge computing environments.

Huang Renjie. Multi-algorithm Fusion Blade Defect Detection Based on Edge Computing Platform. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 6: 15-22. https://doi.org/10.25236/AJETS.2024.070603.

[1] Guo Xiaojie. Research on lightweight algorithm for insulator defect detection by fusing multi-scale features[D]. University of Science and Technology of Taiyuan, 2023

[2] Huanzhang Jin, Yongbo Zhu, Hao Liu, et al. A personalised recommendation algorithm for fusing multi-factors based on edge computing[J]. Journal of Central South University for Nationalities (Natural Science Edition), 2024

[3] M Li, R Zhu, Q Ding, et al. Edge computing-enabled green multisource fusion indoor positioning algorithm based on adaptive particle filter[D]. Cluster Computing, 2023

[4] Jielin Shi. Edge node based energy efficient computing and lightweight transmission strategy for power consumers[D]. Shaanxi: Xi'an University of Architecture and Technology, 2023

[5] Wu Zuozhe. Joint application of real-time intelligent traffic control system and tunnel flood control system based on edge computing + AIOT[J]. Automation Expo, 2022, 39(10):36-38. 

[6] Liu Liang, Ge Haibo, Wei Qiuyue, et al. Real-time object detection algorithm based on edge computing [J]. Sensors and Microsystems, 2023, 42 (12): 115-118. 

[7] H Wang, K Wang, D Lou, et al. Research on power scene anomaly recognition technology based on AI edge depth algorithm video analysis device[J]. Guizhou Electric Power Technology, 2021

[8] Lin Huabiao. Research and implementation of point cloud fusion method based on edge computing of vehicle networking[D]. Guangdong University of Technology, 2021

[9] M. Y. Wu. Research on appearance defect detection method of nylon silk cake based on deep learning [J]. Automation and information engineering, 2021 

[10] XU Yunhua. Detection and segmentation of defects in substation equipment based on deep learning [D]. Shijiazhuang Tiedao University (formerly known as Shijiazhuang Railway University), 2022 

[11] Zhou Yabin. Research on image enhancement and recognition technology for underwater robots [J]. Harbin Engineering University, 2020 

[12] Li Ce, Tang Zhenyan. Machine vision course construction and reform practice for new engineering [J]. Computer Knowledge and Technology: Academic Edition, 2023

[13] Wang Dejuan. Research on significant target detection algorithm based on edge enhancement[J]. Dalian University of Technology, 2019

[14] Zhou Xiaotian, Yang Xiaohui, Zhang Haixia, et al. Joint optimization of task unloading and resource allocation in the UAV-assisted edge computing network [J]. Journal of Electronics and Informatics, 2024, 47(3):1-10.