本文關(guān)鍵詞： BP神經(jīng)網(wǎng)絡(luò) 儲油罐用溫度傳感器 非線性補(bǔ)償　出處：《東北石油大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：大型儲油罐對國家戰(zhàn)略儲備具有非常重要的意義,研究儲油罐溫度場分布規(guī)律,能為實(shí)際生產(chǎn)中制定安全、合理的儲存方案提供科學(xué)指導(dǎo)�，F(xiàn)階段,儲油罐多采用溫度傳感器進(jìn)行溫度數(shù)據(jù)測量,該方法測量性能穩(wěn)定、示值復(fù)現(xiàn)性高,但測量結(jié)果存在一定的非線性誤差。為提高測量精度,有必要對儲油罐用溫度傳感器進(jìn)行非線性補(bǔ)償。本文提出了一種非線性補(bǔ)償方法,即基于人工神經(jīng)網(wǎng)絡(luò)的儲油罐用溫度傳感器非線性補(bǔ)償。本文首先對儲油罐溫度場監(jiān)測系統(tǒng)及其溫度測試數(shù)據(jù)進(jìn)行初步分析,得出儲油罐溫度場分布規(guī)律,依此建立儲油罐內(nèi)部空間區(qū)域劃分模型。通過對多組算例進(jìn)行計(jì)算,對影響特征閥值較明顯的因素采用了加權(quán)平均的方法,得到了罐內(nèi)空間區(qū)域邊界特征閥值,將儲油罐內(nèi)部空間劃分為罐頂區(qū)域、中心區(qū)域和罐底區(qū)域。在此基礎(chǔ)上,根據(jù)BP神經(jīng)網(wǎng)絡(luò)原理和相應(yīng)區(qū)域內(nèi)儲油罐用溫度傳感器的測量數(shù)據(jù),設(shè)計(jì)了用于補(bǔ)償罐頂區(qū)域、中心區(qū)域和罐底區(qū)域溫度傳感器的BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)、函數(shù)等各項(xiàng)參數(shù)。其中網(wǎng)絡(luò)隱含層節(jié)點(diǎn)數(shù)目采用對比網(wǎng)絡(luò)輸出的均方誤差(MSE)的實(shí)驗(yàn)方法確定,網(wǎng)絡(luò)訓(xùn)練用樣本數(shù)據(jù)通過恒溫設(shè)備的測定獲得,并在訓(xùn)練網(wǎng)絡(luò)前對其進(jìn)行了預(yù)處理。本文利用Matlab軟件建立了三個(gè)用于儲油罐用溫度傳感器非線性補(bǔ)償?shù)腂P神經(jīng)網(wǎng)絡(luò)模型,并分別給出三個(gè)BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練后的各項(xiàng)性能。對輸出結(jié)果及溫度場監(jiān)測系統(tǒng)使用數(shù)學(xué)公式法計(jì)算得到的溫度測試結(jié)果與高精度測溫儀采集得到的溫度數(shù)據(jù)進(jìn)行對比分析,結(jié)果表明,網(wǎng)絡(luò)輸出精度較高,性能較好,能夠應(yīng)用在儲油罐溫度場測試領(lǐng)域中。
[Abstract]:Large-scale oil storage tank is of great significance to the national strategic reserve. Studying the temperature field distribution law of oil storage tank can provide scientific guidance for the formulation of safe and reasonable storage plan in actual production. Most oil tanks use temperature sensor to measure temperature data. This method has stable performance and high reproducibility, but there is a certain nonlinear error in the measurement results, in order to improve the accuracy of measurement. It is necessary to make nonlinear compensation for the temperature sensor used in oil storage tank. In this paper, a nonlinear compensation method is proposed. In this paper, the temperature field monitoring system and temperature testing data of oil storage tank are analyzed preliminarily, and the distribution law of temperature field of oil tank is obtained. According to this, the model of internal space zone division of oil storage tank is established. Through the calculation of multiple examples, the weighted average method is used to calculate the factors that affect the characteristic threshold value, and the boundary characteristic threshold value of the inner space area of the tank is obtained. The inner space of the tank is divided into the top area, the center area and the bottom area. On this basis, according to the principle of BP neural network and the measurement data of the temperature sensor used in the oil storage tank in the corresponding area. The structure of BP neural network is designed to compensate the temperature sensor in the top, center and bottom of the tank. The number of nodes in the network hidden layer is determined by the experimental method of comparing the mean square error (MSE) of the network output, and the sample data for network training are obtained by measuring the constant temperature equipment. In this paper, three BP neural network models for nonlinear compensation of temperature sensor used in oil storage tank are established by using Matlab software. The performance of three BP neural networks after training is given respectively. The temperature test results calculated by mathematical formula method and temperature data collected by high precision thermometer are used to input the output results and temperature field monitoring system. A comparative analysis. The results show that the network has higher output precision and better performance. It can be used in the field of oil tank temperature field measurement.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TE972

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本文編號：1450943