本文選題：窯爐控制 + 機(jī)器視覺(jué)��； 參考：《杭州電子科技大學(xué)》2016年碩士論文

【摘要】：窯爐控制系統(tǒng)的研究在國(guó)內(nèi)起步較晚,發(fā)展并不完善,多數(shù)是基于工控機(jī),缺少智能化技術(shù)。目前,嵌入式控制系統(tǒng)已經(jīng)成為工業(yè)控制的主要應(yīng)用,而ePLC(embedded PLC)更是嵌入式控制系統(tǒng)的最新技術(shù)。本文在基于ePLC的窯爐控制系統(tǒng)的基礎(chǔ)上,研究通過(guò)機(jī)器視覺(jué)對(duì)坯體入窯密度的自動(dòng)檢測(cè),實(shí)現(xiàn)對(duì)窯爐的智能化控制,這對(duì)提高我國(guó)窯爐控制的技術(shù)水平具有較大的現(xiàn)實(shí)意義。論文研究了在嵌入式硬件平臺(tái)上,實(shí)現(xiàn)視覺(jué)算法與機(jī)器學(xué)習(xí)算法。在視覺(jué)模塊上集成了多個(gè)圖像算法,用構(gòu)件化的形式封裝在CASS機(jī)器視覺(jué)平臺(tái)上。主要為采集的圖像做圖像預(yù)處理,包括采集圖片的有效信息位置剪裁、圖像灰度立方圖均衡化、圖像閾值二值化以及圖像仿射變換。對(duì)處理后的圖像信息,運(yùn)用改進(jìn)的M-ary SVM(Support Vector Machine)算法進(jìn)行分類(lèi)。對(duì)于分類(lèi)編碼,在信息碼中加入了糾錯(cuò)編碼,增強(qiáng)了其泛化性。在圖像的特征提取方面,提出了整數(shù)權(quán)值的特征提取法,在0-1矩陣圖像里,直接提取16位二進(jìn)制數(shù)作為一個(gè)整數(shù)成為一個(gè)特征值,適用于嵌入式平臺(tái)上的機(jī)器視覺(jué)實(shí)現(xiàn)。最后,在整窯的入窯密度檢測(cè)方面,通過(guò)對(duì)經(jīng)由SVM分類(lèi)的各層的入窯密度,進(jìn)行權(quán)值疊加得到。根據(jù)窯爐內(nèi)部上下部分的加熱環(huán)境不同,在反饋給窯爐控制時(shí),加入權(quán)值計(jì)算總體的熱力需求,再選擇合適的曲線進(jìn)行產(chǎn)品燒制。整個(gè)窯爐控制系統(tǒng)都是基于ePLC開(kāi)發(fā),是智能化、自動(dòng)化更高的控制系統(tǒng)。該系統(tǒng)經(jīng)實(shí)驗(yàn)測(cè)試,能有效通過(guò)機(jī)器視覺(jué)對(duì)坯體入窯密度進(jìn)行自動(dòng)檢測(cè),能實(shí)現(xiàn)對(duì)窯爐的數(shù)碼控制。
[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.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP273

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1 鄭維凱;基于ePLC的窯爐數(shù)碼控制系統(tǒng)的研究與應(yīng)用[D];杭州電子科技大學(xué);2016年

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本文編號(hào)：2009676