基于ePLC的窑炉数码控制系统的研究与应用

发布时间：2018-06-12 12:29

本文选题：窑炉控制 + 机器视觉　；参考：《杭州电子科技大学》2016年硕士论文

【摘要】：窑炉控制系统的研究在国内起步较晚,发展并不完善,多数是基于工控机,缺少智能化技术。目前,嵌入式控制系统已经成为工业控制的主要应用,而ePLC(embedded PLC)更是嵌入式控制系统的最新技术。本文在基于ePLC的窑炉控制系统的基础上,研究通过机器视觉对坯体入窑密度的自动检测,实现对窑炉的智能化控制,这对提高我国窑炉控制的技术水平具有较大的现实意义。论文研究了在嵌入式硬件平台上,实现视觉算法与机器学习算法。在视觉模块上集成了多个图像算法,用构件化的形式封装在CASS机器视觉平台上。主要为采集的图像做图像预处理,包括采集图片的有效信息位置剪裁、图像灰度立方图均衡化、图像阈值二值化以及图像仿射变换。对处理后的图像信息,运用改进的M-ary SVM(Support Vector Machine)算法进行分类。对于分类编码,在信息码中加入了纠错编码,增强了其泛化性。在图像的特征提取方面,提出了整数权值的特征提取法,在0-1矩阵图像里,直接提取16位二进制数作为一个整数成为一个特征值,适用于嵌入式平台上的机器视觉实现。最后,在整窑的入窑密度检测方面,通过对经由SVM分类的各层的入窑密度,进行权值叠加得到。根据窑炉内部上下部分的加热环境不同,在反馈给窑炉控制时,加入权值计算总体的热力需求,再选择合适的曲线进行产品烧制。整个窑炉控制系统都是基于ePLC开发,是智能化、自动化更高的控制系统。该系统经实验测试,能有效通过机器视觉对坯体入窑密度进行自动检测,能实现对窑炉的数码控制。
[Abstract]:The research of kiln control system starts late in our country, and its development is not perfect. Most of the research is based on industrial control computer and lack of intelligent technology. At present, embedded control system has become the main application of industrial control, and ePLC embedded PLC is the latest technology of embedded control system. Based on the control system of kiln based on ePLC, this paper studies how to realize intelligent control of kiln by machine vision, which is of great practical significance to improve the technical level of kiln control in China. This paper studies how to realize vision algorithm and machine learning algorithm on embedded hardware platform. Several image algorithms are integrated into the visual module and encapsulated in the Cass machine vision platform in the form of component. Image preprocessing is mainly done for the collected image, including the effective information position cutting, the image gray cube image equalization, the image threshold binarization and the image affine transformation. The improved M-ary SVM support Vector Machine algorithm is used to classify the processed image information. For classification coding, error correction coding is added to the information code, which enhances its generalization. In the aspect of image feature extraction, an integer weight feature extraction method is proposed. In 0-1 matrix image, 16-bit binary number is directly extracted as an integer to become a feature value, which is suitable for machine vision implementation on embedded platform. Finally, in the whole kiln density detection, through the SVM classification of each layer of the kiln density, the weight of the superposition. According to the different heating environment of the upper and lower parts of the kiln, when feedback is given to the kiln, the weight value is added to calculate the total thermal demand, and then the appropriate curve is selected for the product firing. The whole kiln control system is based on ePLC. It is an intelligent and automatic control system. The system can detect the density of billet into kiln by machine vision and realize the digital control of kiln.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP273

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