基于支持向量机的缺陷识别方法

针对缺陷检测存在的检测手段落后、工序繁琐、准确率低、不易在线实施、受人为因素影响，以及用人工神经网络对小样本事件进行缺陷识别存在的过学习、推广性差等问题，从数据挖掘的角度，提出了直接从形成缺陷的影响因素着手，先消除工艺参数的冗余和噪声，再运用支持向量机分类算法，进行自动缺陷识别的新方法。通过具体的试验表明：该方法具有成本低廉、准确率高、推广性强、容易在线实施等技术优势。

A Novel Method Based on Support Vector Machine for Defect Identification

The traditional fault detection suffers from complicated process, low accurate ratio and off-line implement. The improved methods of defect recognition by artificial neural networks (ANN) can lead to the problems of overfit and bad generalization because of finite samples. With a view of data mining and technique parameters directly, the new approach using support vector machine classification algorithm after removing redundant parameters by rough set theory and eliminating noise of data to identify the defects is discussed. The results of a experiment show that unlike conventional and ANN recognition methods the new technique performs better than conventional evaluation ones with advantages of high efficiency, lower cost, easy implement on-line, excellent generalization and so on. The approach provides a novel technique means for nondestructive defect identification of various products.