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Sensors 2019, 19(3), 723; https://doi.org/10.3390/s19030723

A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography

1
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing Systems, Xi’an Jiaotong University, Xi’an 710049, China
This paper is an extended version of Jiufei Luo, Guang Qiu, Song Feng and Leng Han’s paper “Segmentation of Wear Debris Based on Edge Detection and Contour Classification” published in Proceedings of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi’an, China, 15–17 August 2018.
*
Author to whom correspondence should be addressed.
Received: 30 December 2018 / Revised: 4 February 2019 / Accepted: 6 February 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Abstract

Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms under transmitted light. Reports on the segmentation algorithm of the wear debris ferrograms under reflected light are lacking. In this paper, a wear debris segmentation algorithm based on edge detection and contour classification is proposed. The optimal segmentation threshold is obtained by an adaptive canny algorithm, and the contour classification filling method is applied to overcome the problems of excessive brightness or darkness of some wear debris that is often neglected by traditional segmentation algorithms such as the Otsu and Kittler algorithms. View Full-Text
Keywords: ferrography; classification of contours; segmentation of wear debris ferrography; classification of contours; segmentation of wear debris
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Feng, S.; Qiu, G.; Luo, J.; Han, L.; Mao, J.; Zhang, Y. A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography. Sensors 2019, 19, 723.

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