Morphological Feature Extraction Based on Multiview Images for Wear Debris Analysis in On-line Fluid Monitoring. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Wu, Tonghai; Peng, Yeping; Wang, Shuo; Chen, Feng; Kwok, Ngaiming; Peng, Zhongxiao
• Is Part Of: Tribology transactions. Volume 60:Issue 3(2017)
• Page Start: 408
• Page End: 418
• Publication Date: 2017-05-04
• Subjects: On-line monitoring -- feature extraction -- object detection and tracking -- wear debris analysis; Tribology -- Periodicals; 621
• DOI: 10.1080/10402004.2016.1174325
• Languages: English
• ISSNs: 1040-2004

Abstract

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.