计算机应用研究Issue(5)：1549-1553,5.DOI:10.3969/j.issn.1001-3695.2015.05.068
轴承表面缺陷类型识别算法
Recognition algorithm on bearing surface defect type
摘要
Abstract
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.关键词
轴承/表面缺陷/缺陷识别/Sift 算法/BP 算法Key words
bearing/surface defect/defect recognition/Sift algorithm/BP algorithm分类
信息技术与安全科学引用本文复制引用
陈龙,侯普华,王进,朱文博..轴承表面缺陷类型识别算法[J].计算机应用研究,2015,(5):1549-1553,5.基金项目
国家自然科学基金资助项目（51475309，61472111，51305270）；上海市科研创新资助项目（13YZ071）；上海理工大学国家级项目培育基金和机械工程学院机械制造及其自动化专业一流学科科研专项基金资助项目 （）
