Defect identification of wind turbine blade based on multi‐feature fusion residual network and transfer learning-Reference-Cited by-同舟云学术

Defect identification of wind turbine blade based on multi‐feature fusion residual network and transfer learning

Published:2021-11-24 Issue:1 Volume:10 Page:219-229
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Science & Engineering

Author:

Zhu Jiawei¹^ORCID,Wen Chuanbo¹,Liu Jihui²

Affiliation:

1. College of Electrical Engineering Shanghai Dianji University Shanghai China

2. Maintenance Department Shanghai Electric Wind Power Group Co., Ltd. Shanghai China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1024

Reference32 articles.

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4. An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades

5. CanturkR SlavkovskyE NiezreckiC InalpolatM.Wind turbine blade damage detection using various machine learning algorithms. ASME International Design Engineering Technical Conference/Computer and Information in Engineering Conference (IDETC/CIE) Charlotte NC AUG 21‐24 2016.

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