Abstract
With the development of industry and the consumption of resources, environmental and energy issues have gradually become factors restricting the progress of human civilization. In recent years, people have been continuously researching and trying in the field of exploring and developing new energy. With the gradual consumption of coal, oil and other energy sources, wind energy, which is rich in reserves and has a long history of development, has gradually been developed and utilized by various countries. Wind energy can convert mechanical energy into electrical energy, contributing to the development of large-scale design of wind power blades in my country, and for the design of wind power blades. This paper mainly studies the crack detection problem of wind turbine blades based on the multi-scale fusion technology of mobile information system. By improving the detection efficiency of wind turbine blades, the hidden risks caused by the loss of generator blades can be reduced, and the safety performance of wind power generation can be improved. Multiscale fusion techniques for mobile information systems facilitate crack detection and analysis by analyzing relevant mathematical framework models and concepts such as invariant scale and invariant displacement based on statistical significance. The final result of the study shows that the total number of transverse cracks is 98, the number of identification is 95, the accuracy rate is 96.94%, the total number of longitudinal cracks is 86, the number of identification is 81, the accuracy rate is 94.17%, the total number of transverse cracks is 94.17%, and the total number of longitudinal cracks is 86. It is 84, the number of identifications is 79, and the accuracy rate is 94.05%. It can be seen from the experimental data that the identification accuracy rate of transverse cracks is the highest.
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Acknowledgements
Feature innovation project of colleges and universities in Guangdong province, No. 2020KTSCX163.
Feature innovation project of colleges and universities in Guangdong province, No. 2018KTSCX256.
Guangdong Baiyun university key project, No. 2019BYKYZ02.
Special project in key fields of colleges and universities in Guangdong province, No. 2020ZDZX3009.
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Qi, Y., Tang, H. (2023). Identification and Analysis of Wind Turbine Blade Cracks Based on Multi-scale Fusion of Mobile Information Systems. In: Abawajy, J.H., Xu, Z., Atiquzzaman, M., Zhang, X. (eds) Tenth International Conference on Applications and Techniques in Cyber Intelligence (ICATCI 2022). ICATCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 169. Springer, Cham. https://doi.org/10.1007/978-3-031-28893-7_18
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