Self-adaptive segmentation of oil monitoring ferrographic image based on difference quotient

Guang-rui WEN; Bin XU; Zhi-fen ZHANG; Feng CHEN

doi:10.3788/OPE.20172505.1322

您当前的位置：

首页 >

文章列表页 >

Self-adaptive segmentation of oil monitoring ferrographic image based on difference quotient

Information Sciences | 更新时间：2020-07-07

- Self-adaptive segmentation of oil monitoring ferrographic image based on difference quotient
- Optics and Precision Engineering Vol. 25, Issue 5, Pages: 1322-1330(2017)
- 作者机构：
  
  1.西安交通大学智能仪器与监测诊断研究所, 陕西西安 710049
  2.新疆大学机械工程学院, 新疆乌鲁木齐 830047
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172505.1322
  CLC： TP394.1
- Received：14 November 2016，
  
  Accepted：16 January 2017，
  
  Published：25 May 2017
- 稿件说明：
移动端阅览
Guang-rui WEN, Bin XU, Zhi-fen ZHANG, et al. Self-adaptive segmentation of oil monitoring ferrographic image based on difference quotient[J]. Optics and precision engineering, 2017, 25(5): 1322-1330.
DOI：

Guang-rui WEN, Bin XU, Zhi-fen ZHANG, et al. Self-adaptive segmentation of oil monitoring ferrographic image based on difference quotient[J]. Optics and precision engineering, 2017, 25(5): 1322-1330. DOI： 10.3788/OPE.20172505.1322.

摘要

针对油液监测中铁谱磨粒图像分割阈值难以选取的问题，本文提出一种基于差商的自适应铁谱图像分割算法。首先，将铁谱磨粒灰度图像转换成三维灰度直方图，并对其进行切片分析；然后，引入Newton插值多项式，将不同切片所得的频数作为切片灰度-频数曲线的插值点，基于差商构造第一类可接受函数和第二类可接受函数，结合实验数据确定两类误差，选取同时满足两类误差的最小灰度值作为分割阈值；最后，用本文方法对不同类型的磨粒图像以及添加高斯噪声和椒盐噪声后图像分别进行分割实验，并与经典的迭代阈值法、Otsu算法、最大熵法进行了比较。实验结果表明，本文方法受噪声干扰较小，误检率和漏检率整体优于其他3种算法。对分割所得的磨粒图像进行特征提取，并利用支持向量机进行识别，本文方法对3种故障磨粒识别准确率最高，达到82.86%，虽在运行时间上无明显优势，但综合性能最优，能满足油液监测过程中铁谱图像自适应分割的需求。

Abstract

Aiming at problem that segmentation threshold value of a ferrographic image is difficult to select in oil monitoring

a self-adaptive ferrographic image segmentation algorithm based on difference quotient was introduced. Firstly

the ferrographic abrasive particle image was converted into three-dimensional grey histogram and then a slice analysis was made on it; then

by introducing Newton interpolation polynomial

the pixel number obtained from different slices was took as interpolating point of slice grayscale-frequency curve; the first kind of acceptable function and the second kind of acceptable function were established based on difference quotient

and two kinds of errors were identified by combination of experimental data. The minimum gray value which simultaneously satisfied the two kinds of errors was selected as segmentation threshold value. Finally

segmentation experiments on different types of ferrographic images and ferrographic images with Gaussian noise and salt & pepper noise were conducted to compare the performance of proposed algorithm and three classical algorithms including iterated thresholding method

Otsu algorithm and maximum entropy. The experimental result indicates that the proposed algorithm is rarely interfered by noise and its average false positive rate and average omission rate is overall superior to other three algorithms. Through conducting feature extraction on ferrographic image and identification by support vector machine

it can be found that the proposed method has the highest identification accuracy rate on three faulted abrasive particles

which reaches 82.86%. Although there are no obvious advantages on operation time

but the method has optimal comprehensive property and can meet the requirement for making a self-adaptive segmentation on ferrographic image in the process of oil monitoring.

关键词

Keywords

references

张新明, 尹欣欣, 涂强.动态迁移和椒盐变异融合生物地理学优化算法的高维多阈值分割[J].光学精密工程, 2015, 23(10):2943-2949.

ZHANG X M, YIN X X, TU Q.High-dimensional multilevel thresholding based on BBO with dynamic migration and salt & pepper mutation[J]. Opt. Precision Eng., 2015, 23(10):2943-2949.(in Chinese)

唐庆菊, 刘俊岩, 王扬, 等.基于模糊C均值聚类和Canny算子的红外图像边缘识别与缺陷定量检测[J].红外与激光工程, 2016, 45(9):274-278.

TANG Q J, LIU J Y, WANG Y, et al.. Infrared image edge recognition and defect quantitative determination based on the algorithm of fuzzy C-means clustering and Canny operator[J]. Infrared and Laser Engineering, 2016, 45(9):274-278.(in Chinse)

MEHMET S, BULENT S. Survey over image thresholding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging, 2004, 13(1), 146-165.

王冬冬, 张炜, 金国锋, 等.尖点突变理论在红外热波检测图像分割中的应用[J].红外与激光工程, 2014, 43(3):1009-1015.

WANG D D, ZHANG W, JIN G F, et al.. Application of cusp catastrophic theory in image segmentation of infrared thermal waving inspection[J]. Infrared and Laser Engineering, 2014, 43(3):1009-1015.(in Chinese)

于世强, 戴兴建.基于背景色彩识别的磨粒图像分割方法[J].摩擦学学报, 2007, 27(5):467-471.

YU SH Q, DAI X J. Wear particle image segmentation method based on the recognition of background color[J].Tribology, 2007, 27(5):467-471.(in Chinese)

李绍成, 左洪福, 张艳彬.油液在线监测系统中的磨粒识别[J].光学精密工程, 2009, 17(3):589-595.

LI SH CH, ZUO H F, ZHANG Y B. Wear debris recognition for oil on-line monitoring system[J].Opt. Precision Eng., 2009, 17(3):589-595. (in Chinese)

WEI Y, CHIN K S, MENG H, et al.. Shape classification of wear particles by image boundary analysis using machine learning algorithms[J].Mechanical Systems and Signal Processing, 2016, 72-73:346-358.

袁小翠, 吴禄慎, 陈华伟.基于Otsu方法的钢轨图像分割[J].光学精密工程, 2016, 24(7):1772-1781.

YUAN X C, WU L SH, CHEN H W. Rail image segmentation based on Otsu threshold method[J]. Opt. Precision Eng., 2016, 24(7):1772-1781.(in Chinese)

甄西丰.差商与牛顿插值多项式的承袭性算法[J].华中理工大学学报, 2000, 28(4):35-38.

ZHEN X F. Algorithms with heredity for difference quotient and Newton interpolation polynomial[J]. J. HuazhongUniv. of Sci.& Tech, 2000, 28(4):35-38.(in Chinese)

王兴华, 王何宇.数值差商公式研究[J].中国科学A辑数学.2005, 35(6):712-720.

WANG X H, WANG H Y. A study for formulas of numerical difference quotient[J].Science in China Ser. A Mathematics, 2005, 35(6):712-720.(in Chinese)

GWIDON P S, GWIDON W S, PAWEL P. Automated classification of wear particles based on their surface texture and shape features[J]. Tribology International, 2008, 41(1):34-43.

赵东, 赵宏伟, 于繁华.动态多目标优化的运动物体图像分割[J].光学精密工程, 2015, 23(7):2109-2116.

ZHAO D, ZHAO H W, YU F H. Moving object image segmentation by dynamic multi-objective optimization[J].Opt. Precision Eng., 2015, 23(7):2109-2116.(in Chinese)

Views

449

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Computed tomography image segmentation of cell pole piece via strip attention mechanism

Sand microscopic image segmentation with enhanced tuna swarm optimization exponential entropy

Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism

Automatic segmentation of aggregate images with MET optimized by chaos SSA

Skin lesion segmentation based on high-resolution composite network

Related Author

LIU Zefang

LONG Chao

LIU Xueshuan

HAN Yan

TAN Chuandong

TAN Hui

DUAN Liming

WANG Mengfei

Related Institution

Project Management Center， Equipment Department of Rocket Force

College of Optoelectronic Engineering， Chongqing University

ICT Research Center， Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education， Chongqing University

College of Information， Chang'an University

School of Electrical and Electronic Engineering， Wenzhou University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰