Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation

Xinyu Hang, Xiaoxun Zhu, Xiaoxia Gao, Yu Wang and Longhu Liu

Renewable Energy, 2024, vol. 224, issue C

Abstract: The stability of wind turbines is closely related to the economic benefits of wind energy. To improve the stability of the wind turbine, a comprehensive image diagnosis method based on artificial intelligence method called ‘Multivariate Information Perception You Look Only Once’ (MIP-YOLO) is proposed. MIP-YOLO is an improved algorithm based on YOLOv8 that can classify, detect, segment and evaluate the crack damage level, and can be utilized for monitoring surface cracks on wind turbine blades. To improve the detection capability of small, relatively weak targets such as cracks, Multivariate Information Perception and C2TR modules are put forward. Aim at enhancing the ability of extracting objects with edge features, the Haar wavelet attention (HWA) and C2fGhost modules are proposed. In order to make the model extract features better, C2CBAM module is put forward in this paper. For purpose of solving the problem that some samples in the dataset may have poor quality, wise-IOU is introduced into the model. The detection performance of the proposed method is tested using wind turbine's blade images with cracks taken by Unmanned Aerial Vehicles (UAV). The experiment shows that MIP-YOLO can realize the fault diagnosis of blade effectively and improve the economic benefit of wind energy.

Keywords: Wind energy; Artificial intelligence; MIP-YOLO; Blade surface crack monitoring; Multivariate information perception; Haar wavelet attention (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124002179
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:224:y:2024:i:c:s0960148124002179

DOI: 10.1016/j.renene.2024.120152

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().