A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography
Song Feng,
Guang Qiu,
Jiufei Luo,
Leng Han,
Junhong Mao,
Yi Zhang
Affiliations
Song Feng
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Guang Qiu
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Jiufei Luo
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Leng Han
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Junhong Mao
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
Yi Zhang
School of Advanced Manufacture, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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.
