TY - JOUR
T1 - Research on Defect Identification Method of Engine Lining
AU - Zhao, Yifan
AU - Wu, Qiong
AU - Yu, Jianyi
AU - Gao, Hanjun
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2023/6/15
Y1 - 2023/6/15
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - deep learning
KW - lining
KW - object detection
UR - https://www.scopus.com/pages/publications/85162909658
U2 - 10.1109/JSEN.2023.3274370
DO - 10.1109/JSEN.2023.3274370
M3 - 文章
AN - SCOPUS:85162909658
SN - 1530-437X
VL - 23
SP - 12651
EP - 12662
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 12
ER -