本文關(guān)鍵詞： 光纖壓力傳感器 數(shù)據(jù)融合 二元回歸 粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò) 無線傳輸　出處：《東華大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：傳感器技術(shù),是信息科學(xué)的基礎(chǔ),也是現(xiàn)代物聯(lián)網(wǎng)信息技術(shù)重要支柱之一�，F(xiàn)代工業(yè)持續(xù)發(fā)展,對傳感器的測量精度提出的要求越來越高。隨著光纖工藝的發(fā)展,利用聚合物封裝的反射式光纖壓力傳感器,因其重量輕、尺寸小、傳輸距離遠(yuǎn)、耐腐蝕性能好、抗電磁干擾性能強(qiáng)的特點(diǎn),獲得廣泛應(yīng)用。由于溫度對反射式光纖壓力傳感器測量精度的影響比較大,所以對反射式壓力傳感器的溫度補(bǔ)償進(jìn)行研究。本文采用數(shù)據(jù)融合技術(shù)解決溫度對反射式光纖壓力傳感器測量精度影響的問題,其主要研究內(nèi)容如下:(1)分析了反射式光纖壓力傳感器的工作原理,并對幾種常用數(shù)據(jù)融合算法如多元回歸、傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)以及粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)方法進(jìn)行了論述。(2)針對反射式光纖壓力傳感器測量精度容易受溫度影響較大的問題,采用多傳感器信息融合技術(shù),對反射式光纖壓力傳感器進(jìn)行溫度補(bǔ)償。將反射式光纖壓力傳感器設(shè)置為主傳感器,DS18B20溫度傳感器設(shè)為輔助傳感器,并進(jìn)行了二維溫度標(biāo)定實(shí)驗(yàn)。根據(jù)標(biāo)定實(shí)驗(yàn)數(shù)據(jù),求出未補(bǔ)償前的靈敏度溫度系數(shù)和溫度附加誤差的值分別為8.4808×10~(-3）/℃和32.3120%。(3)建立了多元回歸、傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)以及粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)算法的溫度補(bǔ)償模型,對反射式光纖壓力傳感器進(jìn)行溫度補(bǔ)償,并且比較幾種融合算法的補(bǔ)償效果。(4)本文采用MSP430F149單片機(jī)設(shè)計(jì)出一個(gè)具有無線傳輸功能的實(shí)時(shí)壓力測量及補(bǔ)償系統(tǒng)。本系統(tǒng)主要包含三個(gè)部分:第一部分,將反射式光纖壓力傳感器和溫度傳感器采集數(shù)據(jù)送到上位機(jī);第二部分,nRF24L01短距離無線通信傳輸模塊;第三部分,下位機(jī)對無線模塊傳來的數(shù)據(jù)進(jìn)行處理,并且包括了報(bào)警、顯示等外圍硬件電路。在IAR軟件開發(fā)平臺上運(yùn)用C語言對各個(gè)功能模塊進(jìn)行程序的設(shè)計(jì),通過主程序調(diào)用各個(gè)子模塊,使系統(tǒng)能夠正常實(shí)現(xiàn)工作。研究結(jié)果是,通過利用二元回歸和傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)算法對光纖壓力傳感器進(jìn)行溫度補(bǔ)償后,其溫度靈敏度系數(shù)從原來的8.4808×10~(-3）/℃分別改善為1.9412×10~(-3）/℃和1.0991×10~(-3）/℃,溫度附加誤差由原來的32.3120%,改善為7.4615%和4.1877%,兩種算法對反射式光纖壓力傳感器性都有一定改善;而用粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)方法求出補(bǔ)償后的靈敏度溫度系數(shù)和溫度附加誤差,分別為2.2734×10-4/℃和0.8622%。從以上數(shù)據(jù)可知,粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)把反射式光纖壓力傳感器的靈敏度溫度系數(shù)提升了1個(gè)數(shù)量級,溫度附加誤差提升了2個(gè)數(shù)量級。與前兩種算法比較可知,粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)算法對溫度補(bǔ)償效果更佳。
[Abstract]:Sensor technology is the foundation of information science and one of the important pillars of information technology in modern Internet of things. With the development of modern industry, the requirement of measuring precision of sensor is higher and higher. With the development of optical fiber technology, The reflective fiber optic pressure sensor encapsulated by polymer has the advantages of light weight, small size, long transmission distance, good corrosion resistance and strong resistance to electromagnetic interference. It has been widely used. Because of the influence of temperature on the measuring accuracy of reflective fiber optic pressure sensor, Therefore, the temperature compensation of reflective pressure sensor is studied. In this paper, data fusion technology is used to solve the problem of the influence of temperature on the measurement accuracy of reflective fiber optic pressure sensor. The main research contents are as follows: (1) the working principle of reflective fiber optic pressure sensor is analyzed, and several commonly used data fusion algorithms such as multivariate regression are discussed. The traditional BP neural network and particle swarm optimization BP neural network are discussed. Aiming at the problem that the measurement accuracy of reflective optical fiber pressure sensor is easily affected by temperature, the multi-sensor information fusion technique is adopted. The reflective fiber optic pressure sensor is used to compensate the temperature. The main sensor, the DS18B20 temperature sensor, is set up as the auxiliary sensor, and the two-dimensional temperature calibration experiment is carried out. The values of sensitivity temperature coefficient and temperature additional error before compensation are 8.4808 脳 10 ~ (-3) / 鈩，

本文編號：1521921