Damages Detection of Aeroengine Blades via Deep Learning Algorithms-Reference-Cited by-同舟云学术

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Damages Detection of Aeroengine Blades via Deep Learning Algorithms

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Shuangbao¹^ORCID,Yu Jingyi²^ORCID,Wang Hao³^ORCID

Affiliation:

1. Research Institute of Science and Technology, Civil Aviation University of China, Tianjin, China

2. College of Aeronautical Engineering, Civil Aviation University of China, Tianjin, China

3. Engineering Technology Training Center, Civil Aviation University of China, Tianjin, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10054086.pdf?arnumber=10054086

Reference44 articles.

1. Improved YOLOv5 algorithm for steel surface defect detection;li;J Air Force Eng Univ Nat Sci Ed,2022

2. Research on aero-engine blade crack detection based on image recognition;li;J Univ Electron Sci Technol,2019

3. A review of deep learning based on target detection algorithms;li;Comput Eng,2021

4. Research on leaf damage identification method based on convolutional neural network;zhao;Journal of Civil Aviation University of China,2019

5. Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles

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1. AeroClick: An advanced single-click interactive framework for aeroengine defect segmentation;Expert Systems with Applications;2024-12

2. FC-YOLO: an aircraft skin defect detection algorithm based on multi-scale collaborative feature fusion;Measurement Science and Technology;2024-08-14

3. A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis;Applied Intelligence;2024-08-08

4. Repetition Count: Application to Aero-engine Blade Counting Tasks;2024-08-02

5. SAIT: Harnessing Sparse Annotations and Intrinsic Tasks for Semisupervised Aeroengine Defect Segmentation;IEEE Transactions on Industrial Informatics;2024-08

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