CN111521288B - 一种利用狼群算法的fbg非均匀温度重构方法 - Google Patents

一种利用狼群算法的fbg非均匀温度重构方法 Download PDF

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CN111521288B
CN111521288B CN202010291591.8A CN202010291591A CN111521288B CN 111521288 B CN111521288 B CN 111521288B CN 202010291591 A CN202010291591 A CN 202010291591A CN 111521288 B CN111521288 B CN 111521288B
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邹红波
陶娟
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China Three Gorges University CTGU
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Abstract

一种利用狼群算法的FBG非均匀温度重构方法,首先将FBG传感器粘贴固定在铝合金板上,对铝合金板进行局部受热的方式以使FBG传感器受到一个非均匀温度,通过光谱仪显示非均匀温度下的FBG反射谱波形,再利用狼群算法WPA对FBG反射谱波形进行非均匀温度重构,最后通过PC机显示结果。通过传输矩阵法对非均匀温度分布下的反射光谱进行模拟,这些光谱作为重构时的目标光谱,适应度函数定义为目标光谱和重构光谱之间的误差,使用狼群算法WPA重构温度分布。本发明一种利用狼群算法的FBG非均匀温度重构方法,采用狼群算法WPA,依据非均匀温度下的FBG反射光谱,结合FBG反射光谱分析的传输矩阵法,解决FBG的非均匀温度重构问题。具有重构速度快,精度高等优点。

Description

一种利用狼群算法的FBG非均匀温度重构方法
技术领域
本发明涉及FBG传感器温度测量技术领域,具体涉及一种利用狼群算法的FBG非均匀温度重构方法。
背景技术
当FBG传感器受到非均匀的应变或温度时,FBG的反射光谱可能会出现变形扭曲现象甚至出现多峰,这种情况下,仅仅测量Bragg波长漂移来获得应变或温度数据是不可行的。而FBG传感器在非均匀应变或温度下的反射光谱很容易被光谱分析仪测得,这样,如何根据反射光谱重构出应变或温度分布就成为一个重要的反问题。
解决这个问题有多种方法,最简单的方法是基于强度谱法,但是该方法只适用于单调的应变或温度分布,即一直递增或一直递减的分布。傅里叶变换方法也是可行的,该方法的优点是重构时间较短,缺点是只适用于弱光栅、且需要同时知道光纤光栅的幅度谱和相位谱。一般而言,与幅度谱测量相比较,相位谱的测量需要较为复杂的设备。因而只需要幅度谱的重构方法与需要相位谱的方法相比更具有优势。
近年来,一些进化算法被用来解决如何根据反射光谱重构出应变或温度分布的问题,主要包括模拟退火算法和遗传算法。这些方法重构时仅仅需要光纤光栅的幅度谱,而不需要光纤光栅的相位谱,因而在解决该问题获得了广泛的应用。但是,模拟退火算法和遗传算法的运行时间较长,参数也需要进行优化以跳过局部最优解,因而人们提出了一些改进的算法,主要包括混沌遗传算法、自适应模拟退火算法和模拟退火进化算法。
混沌遗传算法较为复杂,程序运行时间较长,算法收敛速度较慢;自适应模拟退火算法在多变量优化问题中很容易陷入局部极小值,同时该方法在计算时效和精度方面也存在不足;模拟退火进化算法的参数难以控制,不能保证一次就收敛到最优值,一般需要多次尝试才能获得。
发明内容
为解决上述技术问题,本发明提供一种利用狼群算法的FBG非均匀温度重构方法,采用狼群算法WPA,依据非均匀温度下的FBG反射光谱,结合FBG反射光谱分析的传输矩阵法,解决FBG的非均匀温度重构问题。具有重构速度快,精度高等优点。
本发明采取的技术方案为:
一种利用狼群算法的FBG非均匀温度重构方法,首先将FBG传感器粘贴固定在铝合金板上,对铝合金板进行局部受热的方式以使FBG传感器受到一个非均匀温度,通过光谱仪显示非均匀温度下的FBG反射谱波形,再利用狼群算法WPA对FBG反射谱波形进行非均匀温度重构,最后通过PC机显示结果。
通过传输矩阵法对非均匀温度分布下的反射光谱进行模拟,这些光谱作为重构时的目标光谱,适应度函数定义为目标光谱和重构光谱之间的误差,使用狼群算法WPA重构温度分布。
本发明一种利用狼群算法的FBG非均匀温度重构方法,技术效果如下:
1)采用传输矩阵法模拟实际的非均匀温度下FBG反射谱,再使用狼群算法重构非均匀温度分布。该重构方法能够用于实际结构的非均匀温度测量。
2)狼群算法相比于传统的进化算法,如模拟退火算法和遗传算法,具有更好的收敛精度和全局搜索能力,从而具有重构速度快,精度高等优点。
3)使用狼群算法重构FBG非均匀温度,解决了传统FBG传感器只能测量均匀温度的缺陷,是对FBG传感器温度测量领域,包括均匀温度和非均匀温度的进一步拓展。
附图说明
图1为本发明重构流程图;
图2为原始温度和重构温度分布对比图。
具体实施方式
原理分析:
当FBG传感器2受到均匀的应变或温度时,其中心波长随应变或温度发生线性漂移,这种情况下,仅仅需要测量Bragg波长漂移,就能得到外界的应变或温度值。但是当FBG传感器2受到非均匀的应变或温度时,FBG的反射光谱可能会出现变形扭曲现象甚至出现多峰,这种情况下,仅仅测量Bragg波长漂移来获得应变或温度数据是不可行的。然而FBG传感器在非均匀应变或温度下的反射光谱很容易被光谱分析仪测得。本发明采用传输矩阵法对非均匀温度作用下的FBG反射谱进行计算,模拟的实际光谱,再使用狼群算法WPA重构温度分布。
一种利用狼群群算法的FBG非均匀温度重构方法,将FBG传感器2粘贴固定在铝合金板1上,对铝合金板1进行局部受热的方式以使FBG传感器2受到一个非均匀温度,通过光谱仪3显示非均匀温度下的FBG反射谱波形,再利用狼群算法WPA4对FBG反射谱波形进行非均匀温度重构,最后通过PC机5显示结果。
本发明采用铝合金板1,该材料的导热性较好,对温度较为敏感,采用导热性能较好的其他材质板子也可以。将FBG传感器2粘贴固定在铝合金板1上,对铝合金板1进行局部受热的方式以使FBG传感器2受到一个非均匀温度。
首先,使用传输矩阵法对非均匀温度分布下的反射光谱进行计算,这些光谱作为重构时的目标光谱,模拟的测量光谱;适应度函数定义为目标光谱和重构光谱之间的误差。然后,使用狼群算法WPA重构温度分布,取20次运行结果的平均值作为最终的结果。
所述狼群算法WPA包括以下步骤:
步骤1:进行初始化设置,设置的参数有人工狼数量为N,初始位置Xi,最大的游走次数Tmax和迭代次数kmax,步长因子S,距离判定因子ω,探狼、更新比例因子α和β;
步骤2:根据式(1)对探狼游走的位置进行更新,直到探狼适应度Yi>Ylead,用该探狼替换头狼,进行下一步,否则,继续游走直到达到最大次数Tmax后进行下一步;
Figure BDA0002450594660000031
式中,探狼共向h个方向游走,游走的步长为
Figure BDA0002450594660000032
探狼的初始适应度为Yi,探狼在当前位置xid基础上向p(p=1,2,…,h)个方向移动。
步骤3:根据式(2)对猛狼奔袭的位置进行更新,直到探狼适应度Yi>Ylead,用该猛狼替换头狼,否则继续奔袭直到与头狼距离小于dnear时进行下一步;
Figure BDA0002450594660000033
式中,
Figure BDA0002450594660000034
为第k次迭代时头狼的位置,
Figure BDA0002450594660000035
表示猛狼奔袭的步长。
步骤4:根据式(3)对猎物进行围攻,并更新头狼;
Figure BDA0002450594660000036
式中,
Figure BDA0002450594660000037
为猎物的位置即为头狼的位置,
Figure BDA0002450594660000038
为狼群围攻猎物时的步长。
步骤5:整个狼群进行更新;
步骤6:重复进化循环:重复步骤2-5直到符合停止法则,通常该法则为足够小的适应度值或足够大的迭代次数。
所述传输矩阵法,是将一个非均匀的光栅等分为M段,每一段视为均匀的光栅,定义Ri和Si分别为第i段的前向和后向传输模的振幅,电磁波通过这个均匀段可描述为:
Figure BDA0002450594660000041
式中,Ri和Si分别表示第i段光栅的前向和后向传输模的振幅,Ri-1和Si-1分别表示第i-1段光栅的前向和后向传输模的振幅,Fi为第i段光栅的传输矩阵,其表达式如下:
Figure BDA0002450594660000042
式中:
Figure BDA0002450594660000043
k为交流耦合系数,σ为直流自耦合系数,Δz为每段的长度,i表示第i段光栅。于是,整个光栅的传输模可以描述为:
Figure BDA0002450594660000044
式中,R0和S0分别表示光栅初始段的前向和后向传输模的振幅,R和S分别表示整个光栅的前向和后向传输模的振幅,F为整个光栅的传输矩阵,其表达式如下:
Figure BDA0002450594660000045
式中,F1,F2…FM分别表示为第1段,第2段…第M段光栅的传输矩阵,f11,f12,f21,f22为传输矩阵F中的元素。
FBG的反射系数为:
Figure BDA0002450594660000046
当一个FBG传感器受到非均匀的温度T(z),FBG的等价周期Λ(z)可以描述为:
Λ(z)=Λ0[1+(α+ξ)T(z)] 0≤z≤L (9);
式中:L是光栅的长度,Λ0是FBG在常温下的周期,α是热光系数,ξ是热膨胀系数,T(z)表示FBG传感器受到的非均匀温度,z表示光栅轴向上的一点。
则式(5)中的k和σ可以描述为:
Figure BDA0002450594660000047
Figure BDA0002450594660000051
式中:neff是光纤的模式有效折射率,Δn是折射率调制深度,s为条纹可见度,λ为FBG的波长。
对任意给定的参数L,Λ0,neff,Δn,s,α,ξ和T(z),从上述方程可以计算出FBG在非均匀温度下的反射谱。
为了测试狼群算法用于FBG非均匀温度重构的有效性,以二次温度分布为例进行了重构。重构流程图如图1所示,图2给出了原始和重构温度分布。从图2可以看出,使用WPA算法得到的温度分布与原始温度吻合的较好,说明重构精度较高。

Claims (1)

1.一种利用狼群算法的FBG非均匀温度重构方法,其特征在于:首先在FBG传感器(2)上施加非均匀温度,通过光谱仪(3)显示非均匀温度下的FBG反射谱波形,再利用狼群算法WPA(4)对FBG反射谱波形进行非均匀温度重构,最后通过PC机(5)显示结果;当一个FBG传感器受到非均匀的温度T(z),FBG的等价周期Λ(z)描述为:
Λ(z)=Λ0[1+(α+ξ)T(z)] 0≤z≤L (9);
式中:L是光栅的长度,Λ0是FBG在常温下的周期,α是热光系数,ξ是热膨胀系数,T(z)表示FBG传感器受到的非均匀温度,z表示光栅轴向上的一点;
所述狼群算法WPA(4)包括以下步骤:
步骤1:进行初始化设置,设置的参数有人工狼数量为N,初始位置Xi,最大的游走次数Tmax和迭代次数kmax,步长因子S,距离判定因子ω,探狼、更新比例因子α和β;
步骤2:根据式(1)对探狼游走的位置进行更新,直到探狼适应度Yi>Ylead,用该探狼替换头狼,进行下一步,否则,继续游走直到达到最大次数Tmax后进行下一步;
Figure FDA0003504198550000011
式中,探狼共向h个方向游走,游走的步长为
Figure FDA0003504198550000012
探狼的初始适应度为Yi,探狼在当前位置xid基础上向p(p=1,2,…,h)个方向移动;
步骤3:根据式(2)对猛狼奔袭的位置进行更新,直到探狼适应度Yi>Ylead,用该猛狼替换头狼,否则继续奔袭直到与头狼距离小于dnear时进行下一步;
Figure FDA0003504198550000013
式中,
Figure FDA0003504198550000014
为第k次迭代时头狼的位置,
Figure FDA0003504198550000015
为猛狼奔袭的步长;
步骤4:根据式(3)对猎物进行围攻,并更新头狼;
Figure FDA0003504198550000016
式中,
Figure FDA0003504198550000017
为猎物的位置即为头狼的位置,
Figure FDA0003504198550000018
为狼群围攻猎物时的步长;
步骤5:整个狼群进行更新;
步骤6:重复进化循环:重复步骤2-5直到符合停止法则,通常该法则为足够小的适应度值或足够大的迭代次数。
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CN104764414A (zh) * 2015-04-16 2015-07-08 三峡大学 一种利用群算法的fbg非均匀应变重构方法
EP3253095B1 (en) * 2016-05-31 2020-04-08 Advanced Digital Broadcast S.A. An iot-enabled device and a method for manufacturing an iot device
CN106092138A (zh) * 2016-06-06 2016-11-09 东南大学 一种基于微处理器的硅微陀螺仪温度补偿方法

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