基于射线照相的石墨电极缺陷检测与识别

针对石墨电极内部缺陷无损检测难题,提出采用射线检测与深度信念网络相结合的电极缺陷检测方法.为此制作了石墨电极曝光曲线,利用曝光曲线为预制带有不同缺陷的电极试块选取合理的工艺参数、并进行X射线检测.采用去噪增强处理方法改善电极射线图像对比度弱、信噪比低等问题;应用改进的Canny算子边缘检测方法对电极射线图像进行边缘提取,随后对缺陷特征进行选取、计算,并应用深度信念网络对电极缺陷进行智能识别.结果表明:将射线照相技术应用于石墨电极缺陷的检测是可行的;采用改进的Canny算子提取缺陷边缘,质量得到了很大提高,实现了特征参数的准确计算;将深度信念网络应用于电极缺陷的识别,精度可达96.67%,准确率较高.

Defect detection and recognition of graphite electrode based on radiography

In order to solve an existing problem in non-destructive testing of defects in graphite electrode, an electrode defect detection method based on the combination of X-ray detection and deep belief network is proposed in this paper. For this reason, an exposure curve of graphite electrode is made for the graphite electrode, and the exposure curve is used to select reasonable process parameters for prefabricated electrode blocks with different defects, and X-ray inspection is also carried out for the graphite electrode. The de-noising enhancement method is then utilized to improve the weak contrast and low signal-to-noise ratio of the electrode ray image, and the improved Canny operator edge detection method is also adopted to extract the edge of the electrode ray image, then the defect features can be selected and calculated easily. This is due to application of the deep belief network to identify intelligently electrode defects. All results from this research make us believing that it is feasible to apply the radiographic technology to detect graphite electrode defects. The improved Canny operator may be used to extract defect edges. The quality of the extraction has been improved greatly, and the accurate calculation of the characteristic parameters may be realized as a result. The deep belief network can be employed to identify electrode defects with an accuracy of 96.67%, which is a higher accuracy rate indeed.