摘要
针对机械设备磨损状态监测要求,构建了基于显微图像分析的油液在线监测系统。根据系统的光路特点,对磨粒图像进行了基于彩色特征的转换,并通过与背景图像的差值处理来快速提取磨粒目标。基于最小二乘支持向量机设计了两类磨粒分类器,并利用粒子群优化算法对最小二乘支持向量机模型中的参数进行了优化选取。在此基础上,根据磨粒识别体系,设计了磨粒综合分类器。最后,利用铁谱分析技术对系统性能和识别效果进行了检验,结果表明,系统的识别精度达到95%以上,满足磨粒在线监测要求。
For the demands of wear on-line monitoring for mechanical equipment, an on-line oil monitoring system based on microscopic image analysis is constructed. According to the characteristic of system light route, the image of wear debris is converted into gray image based on its color feature, and the wear debris object is extracted by subtracting the background image from the wear debris image. The classifier for two kinds of wear debris is designed based on the least square support vector machines, and the parameters of this model are optimized by Particle Swarm Optimization（PSO） algorithm. Based on this classifier, an integrative wear debris classifier is designed according to the wear debris recognition system. The performance and recognition precision of this system are tested by the ferrography technology. The result shows that the recognition precision of this system is as high as 95 %, which can meet the demand of wear debris on line monitoring.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2009年第3期589-595,共7页
Optics and Precision Engineering
基金
国家863高技术研究发展计划资助项目(No.2006AA04Z427)
中国民航总局科技资金资助项目(No.MHRD0724)
关键词
磨粒
机器磨损
在线监测
图像识别
支持向量机
粒子群优化算法
wear debris
machine wear
on-line monitoring
image recognition
Support Vector Machine（SVM）
Particle Swarm Optimization（PSO） algorithm
