基于yolov5在线可视铁谱图像磨粒多目标识别方法研究  

Research on Multi-target Recognition Method of Wear Debris Based on Yolov5 Online Visible Ferrography Image

作　　者：何铭亮 王建国[1] 范斌 张超[1] 郭向阳 HE Mingliang;WANG Jianguo;FAN Bin;ZHANG Chao;GUO Xiangyang(School of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou Inner Mongolia 014010,China;School of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Hohhot Inner Mongolia 010018,China)

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010 [2]内蒙古农业大学机电工程学院,内蒙古呼和浩特010018

出　　处：《润滑与密封》2023年第5期137-142,共6页Lubrication Engineering

基　　金：国家自然科学基金地区科学基金项目(51965054,51865045);内蒙古自治区自然科学基金面上项目(2021MS05041);内蒙古农业大学高层次人才科研启动项目(NDYB2019-9)。

摘　　要：鉴于在线图像可视铁谱获取的磨粒谱片图像分辨率低,磨粒种类复杂多变,磨粒图像背景复杂等问题,使得磨粒在线智能识别面临挑战。为了实现在线可视铁谱图像磨粒多目标实时检测与识别,提出基于yolov5在线可视铁谱图像磨粒多目标识别方法。以正常磨损磨粒、疲劳磨损磨粒、滑动磨损磨粒、球形磨粒、氧化磨损磨粒、切削磨损磨粒6种磨粒作为研究对象,基于yolov5深度神经网络模型对复杂油液环境下的异常磨损磨粒进行分割与识别。结果表明:基于yolov5算法的磨粒智能识别模型能够实现复杂环境下多目标、多类型磨粒在线实时识别,其识别速度和准确率基本满足油液在线监测需求,为装备在线图像可视铁谱技术工业化应用提供了技术支撑。In view of the low resolution of abrasive grain spectrum images obtained by online visual ferrography,the complex and changeable types of abrasive grains,and the complex background of abrasive grain images,the online intelligent identification of abrasive grains faces challenges.In order to realize the multi-target real-time detection and recognition of abrasive grains in online visual ferrography images,a multi-target recognition method of abrasive grains in online visual ferrography images based on yolov5 was proposed.Taking 6 kinds of wear abrasives,namely normal wear abrasive grains,fatigue wear abrasive grains,sliding wear abrasive grains,spherical abrasive grains,oxidized wear abrasive grains,and cutting wear abrasive grains as the research objects,the segmentation and recognition of abnormal wear particles in complex oil environment based on yolov5 deep neural network model.The results show that the intelligent identification model of abrasive particles based on the yolov5 algorithm can realize the real-time identification of multi-objective and multi-type abrasive particles in complex environment.Its recognition speed and accuracy basically meet the requirements of online monitoring of oil,providing technical support for the industrial application of equipment online image visual ferrography technology.

关 键 词：在线铁谱技术 深度学习 磨粒识别 油液监测 

分 类 号：TH117[机械工程—机械设计及理论]