Next Article in Journal
Resonant Photoacoustic Spectroscopy of NO2 with a UV-LED Based Sensor
Next Article in Special Issue
3D SSY Estimate of EPFM Constraint Parameter under Biaxial Loading for Sensor Structure Design
Previous Article in Journal / Special Issue
Collective Anomalies Detection for Sensing Series of Spacecraft Telemetry with the Fusion of Probability Prediction and Markov Chain Model
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(3), 723;

A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography

School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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)
Full-Text   |   PDF [3866 KB, uploaded 11 February 2019]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top