摘要
针对鼓形滚子表面损伤检测过程中损伤零件数量少、检测准确率及查准率不高的问题,提出一种基于多样本关键损伤区域融合数据增强的轴承鼓形滚子表面损伤检测方法.搭建鼓形滚子表面损伤特征样本检测装置,采集滚子的工作面侧面图像并将侧面图像作为研究对象,根据样本图像中滚子外圆角分布特点对样本核心检测区域进行裁剪.基于聚类预选框及损伤评估函数,从数据集负样本中筛选合适大小与纵横比的关键损伤区域,对所选区域进行不同的仿射变换并与随机采样的正样本进行融合以生成新的增强样本数据.将经过数据增强处理的图像,输入到检测神经网络中进行检测分类.实验表明,采用数据增强的鼓形滚子表面损伤检测模型在测试集上的准确率为95.2%,查准率为97.7%,经过正样本阈值调整后模型最终的查准率为99.8%.该方法融合不同样本的复杂损伤特征,有效地提升了检测准确率以及查准率,满足鼓形滚子表面损伤检测的实际生产需求.
A surface defect detection method of bearing drum-shaped roller was proposed based on multi-sample defective area fusion data augmentation to aim at the problems of defective parts scarcity,low detection accuracy and low precision in process of drum-shaped roller surface defect detection.A drum-shaped roller surface defect sample collection system was built.The side images of the rollers were collected and used as research object.The core detection area was cropped according to distribution characteristics of the outer corner of roller in the sample image.Based on the clustering pre-selection box and the defect assessment function,the key defective area with appropriate size and aspect ratio was selected from the negative samples.The different affine transformations were performed on the selected area.The area was merged with randomly sampled positive samples to generate new data.The processed image was input into the inspection neural network for detection and classification.The experiments showed that accuracy rate and precision rate of the data-enhanced drum roller surface defect detection model were 95.2%and 97.7%respectively.After the threshold was adjusted,the final precision rate was 99.8%.The detection accuracy and precision improved effectively by integrating the complex defects characteristics of the different samples.The actual production requirements of drum roller surface defect detection can be met.
作者
艾青林
崔景瑞
吕冰海
童桐
AI Qing-lin;CUI Jing-rui;LV Bing-hai;TONG Tong(Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education and Zhejiang Province,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第5期1009-1020,共12页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(52075488)
浙江省自然科学基金资助项目(LY20E050023).
关键词
鼓形滚子
表面损伤检测
数据增强
图像分类
特征融合
drum-shaped roller
surface defect detection
data augmentation
image classification
feature fusion
