| 基于支持向量机的轴承表面缺陷检测 |
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| 引用本文： | 涂宏斌,周新建. 基于支持向量机的轴承表面缺陷检测[J]. 现代制造工程, 2006, 0(9): 90-92 |
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| 作者姓名： | 涂宏斌 周新建 |
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| 作者单位： | 华东交通大学CAD/CAM研究室,南昌,330013;华东交通大学CAD/CAM研究室,南昌,330013 |
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| 摘 要： | 提出一种基于支持向量机的轴承表面缺陷检测算法,该算法把轴承中的非缺陷区域和缺陷区域分别看作两种不同的纹理模式,利用主成分分析法(PCA)对图像进行降维处理,然后用支持向量机方法对两类不同的样本采样学习,进行分类判断。实验结果表明,该算法能够较好地实现轴承缺陷的检测分类,有着深入研究的价值。
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| 关 键 词： | 缺陷检测 主成分分析 支持向量机 |
| 文章编号： | 1671-3133(2006)09-0090-03 |
| 修稿时间： | 2006-03-13 |
Bearing defects detection based on support vector machines |
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| Tu Hongbin,Zhou Xinjian. Bearing defects detection based on support vector machines[J]. Modern Manufacturing Engineering, 2006, 0(9): 90-92 |
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| Authors: | Tu Hongbin Zhou Xinjian |
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| Abstract: | Expoles an algorithm about bearing surface defects detection by support vector machines that is the new branch of machine learning,in which the defective area and non-defective area are treated as two different textures and are sampled respectively to be learned,in order to reduce dimension,the image data can be processed by PCA.It is shown that this algorithm works well in defects detection. |
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| Keywords: | Defects detection Principal component analysis Support vector machines |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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