滚动轴承保持架缺陷的图像处理及模式识别方法研究

轻系列滚动轴承保持架由于兜孔直径小、两半保持架之间钉孔距离相对较大等因素导致在铆压过程中易出现变形,造成铆压歪斜缺陷。为此,本文提出了基于图像纹理特征的模式识别方法用于保持架歪斜缺陷的准确识别。首先,改进了轴承图像归一化展开算法,实现了轴承图像展开起点的自动优化选择以避免误分割保持架、铆钉和滚动体;其次,设计了轴承图像保持架区域定位分割算法,准确分离出7个保持架区域;最后,分别提取保持架区域的Hu矩和旋转不变均匀局部二值模式(LBPrPiu,2R)作为纹理特征,并结合PCA降维方法构建轴承保持架缺陷识别的SVM分类模型。结果表明,基于Hu矩和LBPrPiu2,R的SVM模型的正确识别率分别为85%和100%。因此,轴承LBPrPiu2,R特征结合SVM模型对轴承保持架歪斜缺陷具有较好的识别效果。该方法有望为滚动球轴承保持架铆压工艺缺陷的自动识别提供参考。

Research on image processing and pattern recognition of skew defect of antifriction bearing cage

Light series antifriction bearing cages are prone to deformation during the riveting process due to the small diameter of pockets and the relatively large nail hole distance between the two halves, resulting in the defects of riveting skew. Therefore, this paper proposed a pattern recognition method based on image texture features for the accurate identification of cage skew defects. Firstly, a bearing normalization expansion algorithm was improved, which realized the automatic optimization of the starting point of the expansion to avoid mis segmentation of the cages, rivets and rolling elements. Secondly, a bearing image cage localization and segmentation algorithm was designed, and 7 cage regions were accurately separated. Finally, the Hu moment and rotation invariant uniform local binary pattern (LBPriu2P,R) were extracted separately as texture features, and the classification model was constructed by combining PCA and SVM. The results showed that the correct recognition rate of the SVM model based on Hu moment and LBPriu2P,R were 85% and 100% respectively. Therefore, the LBPriu2P,R feature combined with the SVM model has a good recognition effect on the bearing cage skew defect. This method was expected to provide a reference for the automatic identification of the defects in the antifriction bearing cage riveting process.