TY - GEN
T1 - IDENTIFYING WEAR PARTICLES WITH THEIR SHAPE ATTRIBUTE
AU - Laghari, Mohammad S.
AU - Alneyadi, Abdulla M.S.
AU - Alawlaqi, Ahmed Y.
AU - Maraqa, Amr
N1 - Funding Information:
The authors would like to express their appreciation to the UAEU SURE+ 2019 Program and the Faculty of Engineering at UAEU for their financial support.
Publisher Copyright:
© MCCSIS 2022.All rights reserved.
PY - 2022
Y1 - 2022
N2 - Wear particles are produced in all oil-lubricated machines when mechanical parts come in contact. These particles are generated in differing sizes, altering quantities, with specific composition, and morphology. The particles when examined by experts provide vital information concerning machine health to operate and maintain safety, efficiency, and economy. As condition monitoring becomes increasingly important and needs to be carried out regularly, a custom-designed software system is developed to investigate the wear particles by using computer vision and image processing techniques. Identifying abnormal wear at an earlier stage avoids wearing failure modes of components that may lead to the breakdown of the machinery. Conventional methods are used to diagnose wear conditions, however; the use of six morphological attributes of particle size, shape, edge details, color, surface texture, and thickness ratio is the basis of constructing an image analysis system to strengthen wear judgments. The particle shape is one of the most important attributes, which is investigated in this paper. Various parameters are used to extract shape features to identify six wear particle types including severe sliding wear, cutting edge, non-metallic, fatigue, water, and fiber particles. Particle identification is based on eight geometric features which are extracted by the image analysis system and MATLAB code. Tests are conducted to achieve 90% accuracy.
AB - Wear particles are produced in all oil-lubricated machines when mechanical parts come in contact. These particles are generated in differing sizes, altering quantities, with specific composition, and morphology. The particles when examined by experts provide vital information concerning machine health to operate and maintain safety, efficiency, and economy. As condition monitoring becomes increasingly important and needs to be carried out regularly, a custom-designed software system is developed to investigate the wear particles by using computer vision and image processing techniques. Identifying abnormal wear at an earlier stage avoids wearing failure modes of components that may lead to the breakdown of the machinery. Conventional methods are used to diagnose wear conditions, however; the use of six morphological attributes of particle size, shape, edge details, color, surface texture, and thickness ratio is the basis of constructing an image analysis system to strengthen wear judgments. The particle shape is one of the most important attributes, which is investigated in this paper. Various parameters are used to extract shape features to identify six wear particle types including severe sliding wear, cutting edge, non-metallic, fatigue, water, and fiber particles. Particle identification is based on eight geometric features which are extracted by the image analysis system and MATLAB code. Tests are conducted to achieve 90% accuracy.
KW - Computer Vision
KW - Image Processing
KW - Particle Shape Classification
KW - Wear Debris
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M3 - Conference contribution
AN - SCOPUS:85142377666
T3 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
SP - 35
EP - 44
BT - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
PB - IADIS Press
T2 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
Y2 - 19 July 2022 through 22 July 2022
ER -