Defect identification of wind turbine blade based on multi‐feature fusion residual network and transfer learning. Issue 1 (24th November 2021)

• Record Type: Journal Article
• Authors: Zhu, Jiawei; Wen, Chuanbo; Liu, Jihui
• Is Part Of: Energy science & engineering. Volume 10:Issue 1(2022)
• Page Start: 219
• Page End: 229
• Publication Date: 2021-11-24
• Subjects: defect detection -- multi‐feature fusion -- residual network -- transfer learning -- wind turbine blade; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1024
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: As a key part of the wind turbines (WTs), the blade has a direct influence on the efficiency of WT. Because the defect detection technology of WT blade is not widely used, and the robustness of traditional detection methods is poor, this paper proposes a multi‐feature fusion residual network combined with transfer learning. In this paper, the WT blade image dataset is enhanced and constructed to train the convolutional network. Two residual structures of multi‐feature fusion (two feature fusion and three feature fusion) are proposed and compared. At the same time, transfer learning is used to improve training process and accelerate convergence. Compared with several convolutional neural networks based on indices include training loss and testing accuracy, f1‐score and confusion matrix, the method proposed greatly reduces the time while achieving accurate detection. Abstract : This paper proposes a multi‐feature fusion residual network combined with transfer learning, which greatly reduces the time of WT blade defect detection while achieving accurate detection.