CAJ | 학술논문

针对生产和装配过程中轴承表面缺陷检测传统方法的不足，提出一种新的轴承表面缺陷类型识别算法。首先改进 Canny 算子以提高轮廓识别度，将 Sift 算法应用于缺陷区域提取，对轴承表面缺陷图像和无缺陷图像进行 Sift 图像匹配以定位缺陷区域，运用像素点的异或运算以精确提取缺陷区域。选择部分 Hu 矩值和几何特征值准确描述缺陷区域，将其作为 BP 神经网络算法的输入，从而最终识别出缺陷类型。实验表明，该方法提高了识别率，且具有非接触、速度快、精度高和抗干扰能力强等优点，较好地实现了轴承表面缺陷类型的检测。
침대생산화장배과정중축승표면결함검측전통방법적불족，제출일충신적축승표면결함류형식별산법。수선개진 Canny 산자이제고륜곽식별도，장 Sift 산법응용우결함구역제취，대축승표면결함도상화무결함도상진행 Sift 도상필배이정위결함구역，운용상소점적이혹운산이정학제취결함구역。선택부분 Hu 구치화궤하특정치준학묘술결함구역，장기작위 BP 신경망락산법적수입，종이최종식별출결함류형。실험표명，해방법제고료식별솔，차구유비접촉、속도쾌、정도고화항간우능력강등우점，교호지실현료축승표면결함류형적검측。
Aiming at some shortcomings in the traditional recognition methods for the bearing surface defect generated during the process of production and assembly,this paper presented a new bearing surface defect recognition algorithm.Firstly,it put forward an improved Canny operator to enhance the recognition rate,and also applied Sift image matching algorithm on the bearing surface defect extraction to locate the defect area with or without defect by matching the images.It used the pixel XOR operation to extract the defect area precisely and selected part of Hu moment and geometric features values to describe the de-fect area accurately and used as the input data for the BP neural network algorithm.Finally,it identified defect type.Experi-ments show that this method improves the recognition rate,and with the merits of non-contact,fast speed,high accuracy and strong anti-jamming capability,so it can realize the recognition of bearing surface defect type accurately.