CAJ | 학술논문

提出一种利用离线样本学习实现轴承外侧表面缺陷在线快速检测的方法。采用间接照射面光作为光源，通过CCD摄像头采集轴承外侧面图像。在离线情况下：定位轴承侧面的检测区域；分别拟合横纵向的灰度分布规律曲线；并以灰度变换后的样本图像作为检测时的依据。在线检测时：根据学习知识提取出待检测区域、将轴承图像变换成灰度分布均匀的图像；然后对图像进行动态阈值分割并作出决策判断。实验表明，提出的方法能有效地将被测图像变换成灰度均匀的图像；判别一张轴承图像平均时间为20 ms，准确率达98．2％以上，具有较高的实时性和准确性。
제출일충이용리선양본학습실현축승외측표면결함재선쾌속검측적방법。채용간접조사면광작위광원，통과CCD섭상두채집축승외측면도상。재리선정황하：정위축승측면적검측구역；분별의합횡종향적회도분포규률곡선；병이회도변환후적양본도상작위검측시적의거。재선검측시：근거학습지식제취출대검측구역、장축승도상변환성회도분포균균적도상；연후대도상진행동태역치분할병작출결책판단。실험표명，제출적방법능유효지장피측도상변환성회도균균적도상；판별일장축승도상평균시간위20 ms，준학솔체98．2％이상，구유교고적실시성화준학성。
A method is proposed that utilises the offline sample learning to achieve online fast detection of the defects on outside surface of the bearings.The method uses indirect irradiation surface light as the light source,through CCD camera to capture the outside surface images of the bearing.In off-line situation,we locate the detection zone on side of the bearing,and fit the horizontal and vertical gray distribution rule curves respectively;then we use the gray-scale transformed sample image as the basis of detection.In on-line situation,we extract the region to be detected according to the knowledge learned,transform the image of bearing to the image with uniform gray distribution,then conduct dynamic threshold segmentation on the image and make the decision judgment.Experiments show that the proposed method can effectively transform the image to be detected to the image with uniform gray distribution,the average time for distinguishing a bearing image is about 20 ms,and the accuracy can be 98.2% and higher,so the method has high real-time performance and accuracy.