【摘要】：隨著溫度測(cè)量在科學(xué)研究和工業(yè)控制過(guò)程中顯得越來(lái)越重要,人們對(duì)測(cè)溫儀的測(cè)量精度、應(yīng)用范圍要求也越來(lái)越高,因此熒光測(cè)溫技術(shù)應(yīng)運(yùn)而生,迅速得到人們的青睞并且取得長(zhǎng)足的發(fā)展。與其他類型的光纖溫度傳感器相比,熒光光纖溫度傳感器具有很多優(yōu)勢(shì),既可以避免交叉靈敏度、光纖的損耗、環(huán)境的輻射、發(fā)射波的帶寬等因素帶來(lái)的測(cè)溫精度影響,又具有穩(wěn)定性好,可靠性高,使用壽命長(zhǎng)、生產(chǎn)簡(jiǎn)單、成本低等特點(diǎn)。本文首先詳細(xì)闡述了熒光測(cè)溫工作機(jī)理,選擇Ca2Mg Si2O7:Au+作為熒光材料,搭建了基于熒光壽命的熒光光纖測(cè)溫系統(tǒng)。在熒光壽命法測(cè)溫中,由于溫度只與熒光壽命成直接關(guān)系,因此只需要獲得熒光壽命即可,而噪聲干擾是影響熒光壽命的分析計(jì)算關(guān)鍵問(wèn)題。本文提出一種改進(jìn)的小波閾值去噪,既保留傳統(tǒng)的軟硬閾值函數(shù)優(yōu)點(diǎn),又能對(duì)兩者的不足之處改良,從而達(dá)到最優(yōu)的信噪分離效果;通過(guò)matlab仿真分析,對(duì)比去噪后信號(hào)的信噪比,闡述了小波基、層數(shù)、閾值的選擇的重要性。在實(shí)際測(cè)量中,由于去噪后的熒光信號(hào)呈現(xiàn)非指數(shù)形式,需要通過(guò)數(shù)據(jù)擬合來(lái)建立模型得到熒光壽命。通過(guò)對(duì)比傳統(tǒng)的擬合數(shù)據(jù)方法的缺點(diǎn),本文采用人工神經(jīng)網(wǎng)絡(luò)進(jìn)行擬合,并采用小波基函數(shù)代替sigmod激活函數(shù),結(jié)合BP算法和遺傳算法的改進(jìn)算法進(jìn)行網(wǎng)絡(luò)的學(xué)習(xí),不僅提高了擬合精度和收斂速度,而且避免了較大的局部誤差。將新閾值小波去噪應(yīng)用在熒光信號(hào)中,篩選出合適的小波基、閾值和層數(shù),通過(guò)matlab仿真對(duì)比去噪后的信號(hào),可以看出信號(hào)既可以避免出現(xiàn)震蕩點(diǎn),減少了有用信息的損失,變得更加光滑,同時(shí)提高了信號(hào)的信噪比;對(duì)比幾種擬合方法得到的熒光壽命,說(shuō)明了人工神經(jīng)網(wǎng)絡(luò)優(yōu)越性,采用小波神經(jīng)網(wǎng)絡(luò)擬合數(shù)據(jù),不僅提高了信號(hào)曲線的擬合精度,減小了熒光壽命測(cè)量誤差,同時(shí)也提高了溫度測(cè)量精度。實(shí)驗(yàn)結(jié)果表明了本文提出方法的有效性。
[Abstract]:With the increasing importance of temperature measurement in the process of scientific research and industrial control, the measurement accuracy and application range of the thermometer are becoming more and more demanding, so the fluorescence temperature measurement technology emerges as the times require. Quickly get people's favor and make great progress. Compared with other kinds of optical fiber temperature sensors, fluorescent optical fiber temperature sensors have many advantages, such as avoiding the influence of cross sensitivity, optical fiber loss, ambient radiation, bandwidth of emission wave, etc. It also has good stability, high reliability, long service life, simple production, low cost and so on. In this paper, the working mechanism of fluorescence temperature measurement is described in detail. Ca2Mg Si2O7:Au is selected as the fluorescent material, and a fluorescent fiber temperature measuring system based on fluorescence lifetime is built. In the measurement of temperature by fluorescence lifetime method, because the temperature is only directly related to the fluorescence lifetime, it is only necessary to obtain the fluorescence lifetime, and the noise interference is the key problem in the analysis and calculation of the influence of the fluorescence lifetime. In this paper, an improved wavelet threshold de-noising is proposed, which not only retains the advantages of the traditional soft and hard threshold function, but also improves the shortcomings of both, so as to achieve the optimal separation effect of signal and noise. Compared with the signal-to-noise ratio (SNR) of the de-noised signal, the importance of the selection of wavelet basis, number of layers and threshold is expounded. In the actual measurement, because the de-noised fluorescence signal presents a non-exponential form, it is necessary to establish a model to obtain the fluorescence lifetime through data fitting. By comparing the shortcomings of the traditional fitting method, this paper uses artificial neural network to fit, and uses wavelet basis function instead of sigmod activation function, and combines BP algorithm and genetic algorithm to learn the network. It not only improves the fitting accuracy and convergence speed, but also avoids large local errors. The new threshold wavelet denoising is applied to the fluorescence signal, and the suitable wavelet basis, threshold value and layer number are screened out. By comparing the de-noised signal with matlab simulation, it can be seen that the signal can not only avoid the oscillation point, but also reduce the loss of useful information. Compared with the fluorescence lifetime obtained by several fitting methods, the advantage of artificial neural network is explained. Wavelet neural network is used to fit the data, which not only improves the fitting accuracy of signal curve, but also improves the accuracy of signal curve fitting. The measurement error of fluorescence lifetime is reduced and the precision of temperature measurement is improved. The experimental results show the effectiveness of the proposed method.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.7;O174.2;TP183

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本文編號(hào)：2177712