As a component of the Internet of things, high-voltage cables are the power supply infrastructure for the modern development of cities. The operation experience shows that the high-voltage cable has been broken down many times due to the defective operation. At present, due to the limitation of detection technology, the research on detection and identification of defects in high-voltage cables is progressing slowly. Therefore, a new DR technology based on X-ray digital imaging is proposed in this paper to realize real-time detection of defects in the semi-conductive buffer layer of high-voltage cables, and intelligent detection of DR images of high-voltage cables by using image depth processing technology to realize intelligent identification of defects in the buffer layer of power cables. The results show that using the new DR technique proposed in this paper, the accurate and intuitive DR image of high-voltage cable can be obtained quickly, and the intelligent identification of defects can be realized.
With the rapid modernization of the city, power cable has been widely used in the process of urban construction. In the course of cable operation, cable faults become more and more frequent, which has a great impact on national economy and people’s life. The method of digital X-ray imaging can realize the nondestructive testing of power cable body and obtain clear and intuitive X-ray digital image. But it lacks the advanced processing and defect recognition method of X-ray digital image, and can not directly detect and identify the cable body and defect from the original digital image. Therefore, this paper studies the power cable X-ray digital image advanced processing and buffer layer defect intelligent identification technology. By using gray scale processing technology, the original image gray scale range is compressed to the human eye identifiable range, and then the defect identification is carried out. Then the traditional convolution neural network CNN and the full convolution neural network FCN are used to train the image data to realize the intelligent recognition of the power cable buffer layer defect. The research shows that compared with the traditional convolution neural network CNN, the full convolution neural network FCN proposed in this paper has more clear and intuitive recognition effect.
The original X-ray image of the high voltage XLPE insulated cable has low contrast due to its complexity and the limited imaging conditions. Therefore, an X-ray image processing method is proposed to address the high voltage cable’s buffer layer defect detection. One hundred seventy-seven high-resolution images from a cable tunnel are collected. The results indicated that our method could efficiently detect buffer layer defects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.