本文選題：拍頻 + 光纖激光器　； 參考：《河南師范大學(xué)》2017年碩士論文

【摘要】：近年來,光纖光柵傳感技術(shù)在各個領(lǐng)域有廣泛的應(yīng)用,如環(huán)境、農(nóng)業(yè)、地質(zhì)探測、太空等,其傳感解調(diào)方法一直是人們關(guān)注的焦點。普遍商用的方法是采用光纖F-P腔掃描等光干涉解調(diào),這些方法技術(shù)復(fù)雜,成本較高。為降低系統(tǒng)成本,人們提出了外差式解調(diào)的方法。這一技術(shù)普遍采用分布反饋Bragg光纖激光器(DFB)結(jié)構(gòu),利用其雙折射在探測器上形成的拍頻實現(xiàn)傳感解調(diào)。該方法中,實現(xiàn)穩(wěn)定的拍頻需要較復(fù)雜的技術(shù)。繼而,人們提出采用分布反射激光器(DBR)結(jié)構(gòu),利用光纖激光器的腔長變化所形成的多縱模拍頻實現(xiàn)傳感解調(diào)。本文提出了一種實現(xiàn)光纖光柵溫度傳感解調(diào)的方法,通過測量激光拍頻得出,同時對測量的溫度傳感數(shù)據(jù)進行優(yōu)化用所構(gòu)建的三層BP神經(jīng)網(wǎng)絡(luò)模型。該方法分別采用線性啁啾光柵(CFBG)和傳感光纖光柵(FBG)作為光纖激光系統(tǒng)的反饋腔鏡,測量激光器拍頻隨傳感光柵溫度的變化實現(xiàn)溫度傳感。在之前的傳感解調(diào)系統(tǒng)中,我們往往以啁啾光纖光柵(CFBG)的時延特性做為參照標(biāo)準(zhǔn),假設(shè)啁啾光纖光柵(CFBG)具有理想的線性時延,然而在實際應(yīng)用中,由于制作工藝的限制,啁啾光纖光柵(CFBG)時延并非完全線性,且存在抖動。按照線性時延處理測試結(jié)果,存在明顯的系統(tǒng)誤差。因此在測量前應(yīng)根據(jù)系統(tǒng)中所使用啁啾光纖光柵(CFBG)的具體時延特性曲線標(biāo)定相應(yīng)的拍頻頻率以降低該系統(tǒng)誤差。溫度測量誤差是由啁啾光纖光柵(CFBG)非線性時延及時延抖動本身的特性引起的,為了使誤差更小,我們需要選擇一種算法來處理所得的溫度數(shù)據(jù)。BP神經(jīng)網(wǎng)絡(luò)算法具有良好的容錯和非線性映射能力,可逼近任意非線性函數(shù),解決復(fù)雜參量之間的非線性對應(yīng)關(guān)系[1]。利用BP神經(jīng)網(wǎng)絡(luò)算法搭建了三層BP神經(jīng)網(wǎng)絡(luò)模型,實驗中,重復(fù)測量10次,得到10組拍頻頻率/溫度數(shù)據(jù)。將所測頻率數(shù)據(jù)中的9組確定為訓(xùn)練校正集,然后作為網(wǎng)絡(luò)輸入值送入所建模型的輸入層,而輸出值為相對應(yīng)的實際溫度值,對網(wǎng)絡(luò)參數(shù)值進行訓(xùn)練,最終使參數(shù)值達到最佳網(wǎng)絡(luò)結(jié)構(gòu)。用剩下的一組作為測試樣本集進行檢驗,此組數(shù)據(jù)的溫度靈敏度和相關(guān)系數(shù)分別為37.89KHz/℃和99.767%,對該組數(shù)據(jù)訓(xùn)練溫度校正及預(yù)測,其相關(guān)系數(shù)達到99.95%。通過實驗,我們可以得出用三層BP神經(jīng)網(wǎng)絡(luò)算法對實驗所得數(shù)據(jù)進行檢驗,能極大的改善實驗系統(tǒng)的測量精度。本文基于激光拍頻結(jié)合BP神經(jīng)網(wǎng)絡(luò)算法實現(xiàn)溫度傳感,使拍頻解調(diào)這一技術(shù)更實用化。
[Abstract]:In recent years, fiber Bragg grating sensing technology has been widely used in various fields, such as environment, agriculture, geological exploration, space and so on. The common commercial methods are optical fiber F-P cavity scanning equal-optical interference demodulation. These methods are complex and expensive. In order to reduce the system cost, heterodyne demodulation is proposed. The distributed feedback Bragg fiber laser (DFB) structure is widely used in this technique, and the beat frequency formed by its birefringence on the detector is used to demodulate the sensor. In this method, complex techniques are needed to achieve stable beat frequency. Then, a distributed reflection laser (DBR) structure is proposed to demodulate the sensor using multi-longitudinal-mode beat frequency formed by the variation of the cavity length of the fiber laser. In this paper, a demodulation method of fiber Bragg grating (FBG) temperature sensing is proposed, which is obtained by measuring the laser beat frequency and optimizing the measured temperature sensing data using the three-layer BP neural network model. In this method, the linear chirped grating (CFBG) and the sensing fiber Bragg grating (FBGG) are used as the feedback mirrors of the fiber laser system, respectively, and the laser beat frequency is measured with the change of the temperature of the sensing grating to realize the temperature sensing. In previous sensing and demodulation systems, we often take the time delay characteristics of chirped fiber grating (CFBG) as the reference standard, and assume that chirped fiber grating (CFBG) has ideal linear delay. However, in practical applications, due to the limitation of fabrication process, The time delay of chirped fiber grating (CFBG) is not completely linear and jitter exists. According to the test results of linear delay processing, there is obvious systematic error. Therefore, the corresponding beat frequency should be calibrated according to the specific time-delay characteristic curve of chirped fiber Bragg grating (CFBG) used in the system before measurement to reduce the error of the system. The temperature measurement error is caused by the characteristic of the nonlinear delay and jitter of the chirped fiber Bragg grating (CFBG). We need to select an algorithm to process the temperature data. BP neural network algorithm has good fault-tolerant and nonlinear mapping ability, it can approximate any nonlinear function and solve the nonlinear correspondence between complex parameters [1]. A three-layer BP neural network model was built by using BP neural network algorithm. In the experiment, 10 sets of beat frequency / temperature data were obtained by repeated measurement. Nine groups of the measured frequency data are determined as the training correction set, then the input values of the network are fed into the input layer of the established model, and the output values are the corresponding actual temperature values, so the network parameters are trained. Finally, the parameter value reaches the optimal network structure. The temperature sensitivity and correlation coefficient of the data are 37.89KHz/ 鈩，

本文編號：1880447