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人工智能技术在航空发动机孔探检测中的应用进展
引用本文:李续博,王文庆,王凯,黄小朝,陈思远.人工智能技术在航空发动机孔探检测中的应用进展[J].航空工程进展,2023,14(2):12-23.
作者姓名:李续博  王文庆  王凯  黄小朝  陈思远
作者单位:西安邮电大学,西安邮电大学,中国航空研究院 第六二四研究所,深圳航空西安分公司,深圳航空西安分公司
基金项目:陕西省重点研发计划(2018ZDXM-GY-039)
摘    要:孔探是当前航空发动机检修过程中应用最多的无损检测方法,也是孔探图像的唯一获取途径。近年来,深度学习等人工智能方法开始被应用到航空发动机损伤分类、检测中,为实现航空发动机检修智能化提出了一些现行有效的方法,具有重要的工业应用价值。本文概述了航空发动机孔探检测的发展和优缺点,综述了专家系统和机器学习人工智能方法在发动机孔探图像方面的应用进展,总结了基于孔探图像实现航空发动机孔探检测智能化面临的一些挑战。

关 键 词:航空发动机、孔探、专家系统、机器学习、深度学习
收稿时间:2022/4/12 0:00:00
修稿时间:2022/7/22 0:00:00

Advances in Application of Artificial Intelligence Technology in Aero-engine Borescope Inspection
LiXubo,WangWenqing,Wangkai,HuangXaochao and ChenSiyuan.Advances in Application of Artificial Intelligence Technology in Aero-engine Borescope Inspection[J].Advances in Aeronautical Science and Engineering,2023,14(2):12-23.
Authors:LiXubo  WangWenqing  Wangkai  HuangXaochao and ChenSiyuan
Institution:Xian University of Posts andTelecommunications,Xian University of Posts andTelecommunications,,,
Abstract:Borescope is currently the most applied non-destructive testing method in the process of aero-engine inspections and is the only way to obtain borescope images. In recent years, artificial intelligence methods such as deep learning have been applied to aero-engine damage classification and detection, some effective methods are proposed to achieve intelligent inspection of aero engines which have significant value for industrial applications. Summarizes the benefits and the disadvantages of aero-engine borescope inspection and its development, the progress in the application of artificial intelligence methods such as expert system and machine learning to engine borescope detection images, and some of the challenges in achieving intelligent aero-engine borescope inspection.
Keywords:Aero engines  borescope  expert system  machine learning  deep learning
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