Morphological feature extraction based on multiview images for wear debris analysis in on-line fluid monitoring

Tonghai Wu, Yeping Peng, Shuo Wang, Feng Chen, Ngaiming Kwok, Zhongxiao Peng

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)408-418
Number of pages11
JournalTribology Transactions
Volume60
Issue number3
DOIs
Publication statusPublished - 2017

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