CHENG Ya-ru 1 , LI Tian 2 , XUE Hui 2 , LI Hong-ying 2 , WANG Dan 3,4 , TANG Jun-lei 1.Research Progress on Aeroengine Blade Surface Damage and Inspection[J].航空发动机,2024,50(2):32-44
Research Progress on Aeroengine Blade Surface Damage and Inspection
Key Words:blade damage  nondestructive inspection  machine vision  deep learning  aeroengine
Author NameAffiliation
CHENG Ya-ru 1 , LI Tian 2 , XUE Hui 2 , LI Hong-ying 2 , WANG Dan 3,4 , TANG Jun-lei 1 College of Chemistry and Chemical Engineering 1 Institute of Carbon Neutrality 4 Southwest Petroleum University:Chengdu 610500 China2.AECC Aero Science and Technology Co.LTD.Chengdu 610503China3.School of Electrical and Automation Engineering Changshu Institite of TechnologyChangshu 215500China 
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Abstract:The working environment of aeroengine blades is extremely harsh, leading to various types of surface damage. Surface inspection at the early stages of damage can effectively prevent blade failure and fracture caused by damage propagation. Currently, the inspection and evaluation of engine blade surface damage heavily rely on manual operation, which not only lacks efficiency but also suffers from susceptibility to human factors. In order to achieve efficient and accurate inspection of engine blade surface damage, from the forms of blade failure, the damage mechanisms under both non-operating and operating conditions are reviewed, with emphasis on methods com? monly used in blade surface damage inspection, such as eddy current and penetration. Additionally, this paper summarizes machine vision- based inspection technology while addressing the challenge posed by dataset scarcity and singularity in machine vision applications. It is believed that collecting extensive datasets and further enhancing evaluation criteria are key directions of future research on engine blade surface damage inspection systems.
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