Affiliation:
1. College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
2. School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Abstract
The fast and accurate detection of wind turbine gearbox surface defects is crucial for wind turbine maintenance and power security. However, owing to the uneven distribution of gear surface defects and the interference of complex backgrounds, there are limitations to gear-surface defect detection; therefore, this paper proposes a multiscale feature reconstruction-based detection method for wind turbine gearbox surface defects. First, the Swin Transformer was used as a backbone network based on the PSPNet network to obtain global and local features through multiscale feature reconstruction. Second, a Feature Similarity Module was used to filter important feature sub-blocks, which increased the inter-class differences and reduced the intra-class differences to enhance the discriminative ability of the model for similar features. Finally, the fusion of contextual information using the pyramid pooling module enhanced the extraction of gear surface defect features at different scales. The experimental results indicated that the improved algorithm outperformed the original PSPNet algorithm by 1.21% and 3.88% for the mean intersection over union and mean pixel accuracy, respectively, and significantly outperformed semantic segmentation networks such as U-Net and DeepLabv3+.
Funder
Natural Science Basic Research Program of Shaanxi Province, China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
1 articles.
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