TY - JOUR
T1 - Morphological feature extraction based on multiview images for wear debris analysis in on-line fluid monitoring
AU - Wu, Tonghai
AU - Peng, Yeping
AU - Wang, Shuo
AU - Chen, Feng
AU - Kwok, Ngaiming
AU - Peng, Zhongxiao
PY - 2017
Y1 - 2017
N2 - Wear state is an important indicator of machinery operation condition that reveals whether faults have developed and maintenance should be scheduled. Among the available techniques, vision-based on-line monitoring of wear particles in the lubricant circuit is preferred, where three-dimensional particle characterizations can be obtained for wear mode analysis. This article presents the application of an imaging system that captures wear particles in lubricant flow and the development of image processing procedures for multiview feature extraction. In particular, a framework including background subtraction, object segmentation, and debris tracking was adopted. Particle features were then used in a comprehensive morphological description of wear debris. Experiments showed that the system is able to produce a feasible and reliable indication of wear debris characteristics for machine condition monitoring.
AB - Wear state is an important indicator of machinery operation condition that reveals whether faults have developed and maintenance should be scheduled. Among the available techniques, vision-based on-line monitoring of wear particles in the lubricant circuit is preferred, where three-dimensional particle characterizations can be obtained for wear mode analysis. This article presents the application of an imaging system that captures wear particles in lubricant flow and the development of image processing procedures for multiview feature extraction. In particular, a framework including background subtraction, object segmentation, and debris tracking was adopted. Particle features were then used in a comprehensive morphological description of wear debris. Experiments showed that the system is able to produce a feasible and reliable indication of wear debris characteristics for machine condition monitoring.
UR - https://hdl.handle.net/1959.7/uws:63917
U2 - 10.1080/10402004.2016.1174325
DO - 10.1080/10402004.2016.1174325
M3 - Article
SN - 1040-2004
VL - 60
SP - 408
EP - 418
JO - Tribology Transactions
JF - Tribology Transactions
IS - 3
ER -