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Research on Defect Identification Method of Engine Lining

  • Yifan Zhao
  • , Qiong Wu
  • , Jianyi Yu
  • , Hanjun Gao*
  • *Corresponding author for this work
  • Beihang University
  • China Aerospace Science and Industry Corporation

Research output: Contribution to journalArticlepeer-review

Abstract

The engine lining can prevent fuel from debonding with the insulating layer, isolate heat, prevent combustion, and buffer stress. Although its proportion is small, the bonding performance of the lining is directly related to whether the engine can maintain a complete structure and also determines the stability of fuel combustion. How to identify the defects of the formed lining and complete the comprehensive detection of the lining is of great significance to maintain the stability of the engine structure. In this study, an image acquisition system composed of industrial cameras, line-array lenses, and line-light sources is used to achieve the integrity acquisition of the lining surface image. Then, through self-made small-scale data sets for the lining image, the backbone network design, and the improvement of existing detectors and network components, the high-precision identification of image defects is achieved.

Original languageEnglish
Pages (from-to)12651-12662
Number of pages12
JournalIEEE Sensors Journal
Volume23
Issue number12
DOIs
StatePublished - 15 Jun 2023

Keywords

  • Convolutional neural network
  • deep learning
  • lining
  • object detection

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