TY - GEN
T1 - Wear debris shape classification
AU - Laghari, Mohammad Shakeel
AU - Hassan, Ahmed
AU - Noman, Mubashir
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - Wear debris is produced in all machines containing moving parts. Wear debris or particles separate from these moving parts because of close contacts and friction and are contained in oil in an oil-wetted system. Analysis of wear debris provides important information about the condition of a machine. The produced particles come in different shapes, sizes, colors, and surface texture. This paper describes the morphological analysis of wear particles by using computer vision and image processing techniques. The aim is to classify these particles according to their shape attributes. Four particle shapes are classified by using Histogram of Oriented Gradients (HOG) and shape attributes including eccentricity, extent, major and minor axis length, equiv-diameter, and centroid distance. The shape classification can be used to identify origin of particle generation and thus predict wear failure modes in engines and other machinery. The objective of particle classification obviates reliance on visual inspection techniques and the need for specialists in the field.
AB - Wear debris is produced in all machines containing moving parts. Wear debris or particles separate from these moving parts because of close contacts and friction and are contained in oil in an oil-wetted system. Analysis of wear debris provides important information about the condition of a machine. The produced particles come in different shapes, sizes, colors, and surface texture. This paper describes the morphological analysis of wear particles by using computer vision and image processing techniques. The aim is to classify these particles according to their shape attributes. Four particle shapes are classified by using Histogram of Oriented Gradients (HOG) and shape attributes including eccentricity, extent, major and minor axis length, equiv-diameter, and centroid distance. The shape classification can be used to identify origin of particle generation and thus predict wear failure modes in engines and other machinery. The objective of particle classification obviates reliance on visual inspection techniques and the need for specialists in the field.
KW - Computer vision
KW - Histogram of oriented gradients
KW - Image processing
KW - Particle shape classification
KW - Wear debris
UR - http://www.scopus.com/inward/record.url?scp=85089610516&partnerID=8YFLogxK
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U2 - 10.1007/978-981-15-4775-1_23
DO - 10.1007/978-981-15-4775-1_23
M3 - Conference contribution
AN - SCOPUS:85089610516
SN - 9789811547744
T3 - Lecture Notes in Electrical Engineering
SP - 209
EP - 217
BT - Modelling, Simulation and Intelligent Computing - Proceedings of MoSICom 2020
A2 - Goel, Nilesh
A2 - Hasan, Shazia
A2 - Kalaichelvi, V.
PB - Springer
T2 - International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2020
Y2 - 29 January 2020 through 31 January 2020
ER -