Article(id=1200070658370663103, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1200070652918072201, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20240210, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1724688000000, revisedDateStr=2024-08-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1764048741190, onlineDateStr=2025-11-25, pubDate=1731600000000, pubDateStr=2024-11-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764048741190, onlineIssueDateStr=2025-11-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764048741190, creator=13701087609, updateTime=1764048741190, updator=13701087609, issue=Issue{id=1200070652918072201, tenantId=1146029695717560320, journalId=1189918454225211397, year='2024', volume='', issue='11', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764048739890, creator=13701087609, updateTime=1764049437840, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1200073580382879882, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1200070652918072201, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1200073580382879883, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1200070652918072201, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=20, endPage=24, ext={EN=ArticleExt(id=1200070659628954331, articleId=1200070658370663103, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Research on Detection Method of Leakage in Automotive Exhaust Pipes Based on Distributed Fiber Optic Vibration Sensing Technology, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To tackle the challenge of exhaust gas leak detection, this paper proposes an exhaust gas leakage detection method based on distributed fiber optic vibration sensing technology. Firstly, sensors are mounted on the vehicle’s exhaust pipe to detect variations in both intensity and phase of the optical fiber light signal, which arise from local pressure disturbances associated with exhaust gas leaks. A distributed vibration sensing system that utilizes a dual Mach-Zehnder interferometer setup is applied with a differential cross-correlation algorithm for demodulating vibrations captured at both ends. Secondly, a cross-correlation delay algorithm is used to precisely pinpoint any leakage locations. In simulation test, leak scenarios are emulated using a piezoelectric ceramic device along with a designated fiber length of 2 m, achieving remarkable positioning accuracy of 0.01 m and validating the feasibility of this proposed method.

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针对汽车尾气泄漏检测问题,提出一种基于分布式光纤振动传感检测技术的尾气泄漏检测方法。首先,通过在汽车尾气管上安装传感器捕捉尾气泄漏引起的局部气压扰动导致的光纤光信号强度和相位变化,采用基于双马赫-曾德尔干涉结构的分布式振动传感系统和微分交叉算法解调双端振动信号,其次,利用互相关时延算法精确定位泄漏位置,在仿真测试中通过压电陶瓷装置模拟泄漏情况,设置2 m光纤长度,实现了0.01 m的定位精度,验证了所提出方法的可行性。

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基于分布式光纤振动传感技术的汽车排气管漏气检测方法研究
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杨鉴 1 , 陈明 2
汽车工程师 | 2024,(11): 20-24
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汽车工程师 | 2024, (11): 20-24
基于分布式光纤振动传感技术的汽车排气管漏气检测方法研究
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杨鉴1, 陈明2
作者信息
  • 1 安徽理工大学, 淮南 232001
  • 2 深圳大学, 深圳 518060
Research on Detection Method of Leakage in Automotive Exhaust Pipes Based on Distributed Fiber Optic Vibration Sensing Technology
Jian Yang1, Ming Chen2
Affiliations
  • 1 Anhui University of Science and Technology, Huainan 232001
  • 2 Shenzhen University, Shenzhen 518060
出版时间: 2024-11-15 doi: 10.20104/j.cnki.1674-6546.20240210
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针对汽车尾气泄漏检测问题,提出一种基于分布式光纤振动传感检测技术的尾气泄漏检测方法。首先,通过在汽车尾气管上安装传感器捕捉尾气泄漏引起的局部气压扰动导致的光纤光信号强度和相位变化,采用基于双马赫-曾德尔干涉结构的分布式振动传感系统和微分交叉算法解调双端振动信号,其次,利用互相关时延算法精确定位泄漏位置,在仿真测试中通过压电陶瓷装置模拟泄漏情况,设置2 m光纤长度,实现了0.01 m的定位精度,验证了所提出方法的可行性。

尾气泄漏检测  /  分布式光纤传感  /  双马赫-曾德尔干涉仪  /  互相关时延定位

To tackle the challenge of exhaust gas leak detection, this paper proposes an exhaust gas leakage detection method based on distributed fiber optic vibration sensing technology. Firstly, sensors are mounted on the vehicle’s exhaust pipe to detect variations in both intensity and phase of the optical fiber light signal, which arise from local pressure disturbances associated with exhaust gas leaks. A distributed vibration sensing system that utilizes a dual Mach-Zehnder interferometer setup is applied with a differential cross-correlation algorithm for demodulating vibrations captured at both ends. Secondly, a cross-correlation delay algorithm is used to precisely pinpoint any leakage locations. In simulation test, leak scenarios are emulated using a piezoelectric ceramic device along with a designated fiber length of 2 m, achieving remarkable positioning accuracy of 0.01 m and validating the feasibility of this proposed method.

Exhaust gas leakage detection  /  Distributed fiber optic sensing  /  Dual-Mach-Zehnder interferometer  /  Cross-correlation delay positioning
杨鉴, 陈明. 基于分布式光纤振动传感技术的汽车排气管漏气检测方法研究. 汽车工程师, 2024 , (11) : 20 -24 . DOI: 10.20104/j.cnki.1674-6546.20240210
Jian Yang, Ming Chen. Research on Detection Method of Leakage in Automotive Exhaust Pipes Based on Distributed Fiber Optic Vibration Sensing Technology[J]. Automotive Engineer, 2024 , (11) : 20 -24 . DOI: 10.20104/j.cnki.1674-6546.20240210
燃油汽车排气管漏气的原因主要有:排气管长期接触尾气及雨水、盐分、尘土等导致排气管腐蚀、形成锈洞;车辆行驶过程中的振动或外力撞击导致排气管出现裂缝或断裂;排气管生产过程中的制造缺陷或质量问题[1]。汽车排气管漏气将造成排放超过相关标准并降低车辆性能,因此,及早识别漏气及其位置具有重要意义。
超声波传感器、压力传感器和热像仪等多种传感器已在汽车排气管漏气检测中应用。超声波传感器灵敏度高,但易受发动机噪声干扰[2];压力传感器可以检测压力变化,但无法精确定位泄漏点[3-4];热像仪受环境温度变化影响较大,且成本较高。上述技术各有优势及局限性,但均未能完全满足高精度、低成本、高可靠性的检测需求。
本文以分布式光纤振动传感技术为基础,提出一种汽车排气管漏气检测方法。通过捕捉汽车尾气泄漏时压强变化引起的光纤中光信号周期性相位变化,采用基于双马赫-曾德尔干涉结构的分布式振动传感系统和微分交叉算法解调双端振动信号,利用互相关算法对漏气位置进行定位。最后,对算法的性能进行仿真验证。
汽车排气管内部的压强(即排气背压)随车速和发动机运转工况变化。理想情况下,大多数乘用车排气背压保持在0.4~0.6 MPa范围内,本文以0.5 MPa作为排气管的平均压强[5]。当管道由于内部压力过大或其他原因破裂时,破裂瞬间会产生压力波,可以通过爆炸力学的相关理论进行估算,影响因素包括破裂口尺寸、破裂瞬间的压力、管道材质特性、破裂后的气体流动特征及周围结构的具体情况。
管道破裂前承受内压导致的应力可使用环向应力公式(针对圆形截面管道)计算[6]
${\sigma }_{\theta }=\frac{PD}{2t}$
式中:σθ为环向应力,P为管道内部的平均压强,D为管道直径,t为管道壁厚。
将一根高温环境下光学性能不变的光纤环形缠绕于排气管外,如图1所示。当排气管某处发生泄漏时,该处的环向应力会对光纤产生足够大的局部扰动。
光纤受到压力、温度或弯曲等环境因素影响时,其物理特性(如折射率、尺寸等)会发生变化,进而影响光波在光纤中的传播路径,特别是其相位。
以马赫-曾德尔干涉仪为例,外界发生扰动使传感光纤中传输的光产生相移[7],如图2所示,其中,EiEO分别为输入光功率和经过传输后发生损耗的输出光功率,ε为外界扰动施加的微小应变,C1、C2分别为光信号分束端和发生干涉后的耦合端。相移可结合干涉仪的工作原理进行检测,其中光被分为两路,经过不同的路径后再进行一次干涉。环境扰动对光纤的影响可通过分析干涉图样进行精确测量。
特定频率的光波在通过光纤后经历的相位变化ϕ为:
$\begin{array}{c}\varphi =n{k}_{0}L=\beta L\end{array}$
式中:n为折射率;k0=2π/λ0为光波的波长;L为光纤总长度;β为光波的传播常数,受光纤的有效折射率和波长影响;λ0为传输光波长。
当存在外界扰动时,光波在光纤中的附加相位变化Δϕ可表示为:
$\begin{array}{c}\Delta \varphi =\beta \Delta L+L\Delta \beta =\beta L\frac{\Delta L}{L}+L\frac{\partial \beta }{\partial n}\Delta n+L\frac{\partial \beta }{\partial D}\Delta D\end{array}$
式中:ΔL、Δβ分别为外界因素引起的相对光程差和相对传播常数变化;Δn为弹光效应引起的折射率变化;ΔD为光纤直径的变化,对应于波导效应。
评估光纤传感器中相位变化的物理原因时,需注意3个关键因素:光纤弹性变形导致的相位变化;弹光效应对相位的影响,即光纤会产生折光率变化;纤芯直径调整产生的泊松效应[8]
光纤传感系统以3×3耦合器解调方法为基础,由1×2耦合器、3×3耦合器及3个光电探测器组成信号解调部分,与两条长度不同的光纤臂连接后组建一台马赫-曾德尔干涉仪。信号光经过1×2耦合器进入马赫-曾德尔干涉仪并进行干涉,再经3×3耦合器将光信号划分为三路,相位差为2π/3,第m路光信号的输出强度Im为:
$\begin{array}{c}{I}_{m}=A+B·cos\left[\varphi \left(t\right)+\left(m-1\right)·\frac{2\pi }{3}\right]\end{array}$
式中:A为平均光强;B为干涉条纹幅值;ϕ(t)为两个位置的相位差,可通过微分交叉算法求解[9],算法如图3所示。
图3中,I1I2I3为进入光电探测器的光强,abc为3×3耦合器的耦合系数,SD分别为双端拟合结果,ϕout为微分算法最终输出光强。
3路光信号由3台光电探测器分别转换成电信号,再使用采集卡对数据进行采集,经计算机处理后输出结果。
为外界扰动信号建立传输信号模型,如图4所示。
在感测区内,传感光纤通过将外界产生的扰动信号调制成光信号的相位,再利用干涉原理将相位变化进一步转化为光强变化。最后,光强信号经光电探测器转换为电信号,并对其进行处理。系统信号处理模块的关键是从电压信号中精确提取振动信号信息。
双马赫-曾德尔型分布式光纤传感系统如图5所示。
使用线偏振光激光器产生波长为1 550 nm的光作为探测光,光输出功率约为4 mW,线宽为50 kHz。首先,激光器通过光隔离器优化偏振状态,以提高信号的稳定性。包含振动信息的信号光经1×2耦合器,再经另一条1×2耦合器传播,并被分成两个等功率的光束,均耦合到相同的单模光纤上。在传感光纤中,两束光沿相反方向传播并穿过整个光纤,然后进入相位解调阶段。
外部扰动作用于传感区域特定点时,顺时针和逆时针方向传递的光束会产生相同特征的相位变化。两个振动调制光信号到达时间的差值可以通过互相关算法计算,利用时间差推测出振动位置[10]。其表达式[11]为:
$\begin{array}{c}\left\{\begin{array}{l}x=\frac{c{t}_{A}}{n}\\ 2L-x=\frac{c{t}_{B}}{n}\end{array}\right.\end{array}$
式中:x为振动点与系统前端耦合器的距离,c为真空中的光速,tA为探测器1采集到逆时针方向振动信号的时间,tB为探测器2采集到顺时针方向振动信号的时间。
检测端接收到的两个干涉光束的时间差为:
$\begin{array}{c}\Delta t={t}_{B}-{t}_{A}=\frac{n\left(2L-x\right)}{c}-\frac{nx}{c}=\frac{2n}{c}\left(L-x\right)\end{array}$
解得:
$\begin{array}{c}x=L-\frac{c\Delta t}{2n}\end{array}$
由式(7)可知,对于已建成的系统,定位误差d只与时延测量误差td有关:
$\begin{array}{c}d=\frac{c}{2n}\left(\Delta t-{t}_{d}\right)\end{array}$
为精确获得泄漏点位置,需对光纤进行环形标记:光纤绕排气管,第1圈标记为A01,第2圈标记为A02,以此类推。如图6所示,以A01-0为起始点,逆时针间隔45°依次标记一个点位,如A01-0、A01-45和A01-90等。
当光纤受到尾气泄漏的气压扰动时,可通过解调双端扰动的相位信息,利用互相关时延算法定位振动位置,准确找到汽车尾气的泄漏点。
由于实际应用时需将光纤紧贴于排气管,为评估本文提出的传感器的性能,根据排气管长度,设置光纤总长度为2 m。在1.2 m处对传感光纤施加扰动,模拟排气管内部压强,将压电陶瓷振动频率设置为0.1 Hz,施加的应力为11.25 MPa,振动通过压电陶瓷传递到传感光纤。马赫-曾德尔干涉仪的实测时域波形如图7所示。采集时间窗为50 s,示波器的采样频率为1 GHz,对应采样间隔为0.01 m。采样间隔应小于系统的定位误差,该系统理想情况下定位误差在0.01 m以内。
与使用两个不同波长激光源的传感系统不同,本文提出的传感器只使用一个激光源。两个马赫-曾德尔干涉仪经历了相同的相位变化,因此其光程差的扰动变化是相似的,输出波形应该具有相似的变化趋势。从图7中可以观察到,两端具有相似的波形,且只有微小的时间差。此外,波形强度差异是考虑了外界低频噪声信号及所使用的连接器和耦合器损耗噪声导致的。考虑到两个马赫-曾德尔干涉仪仿真波形良好的相似性和内在相关性,因此不需进行复杂的时域分析,采样数据可直接计算相互关系[12],进而通过检索两个振动信号之间的时间延迟来确定振动位置。
对两个马赫-曾德尔干涉仪波形进行归一化后[13],计算得到的互相关结果如图8所示。根据式(9)计算可得时间延迟为0.21 ns,则振动位置在1.2 m左右,接近真实扰动点。
为解决汽车尾气泄漏检测问题,在马赫-曾德尔干涉仪分布式光纤传感器理论分析的基础上,本文提出并设计了基于双马赫-曾德尔干涉仪结构的光纤传感系统,说明了干涉测量技术与基于互相关算法的时延差定位方法,并分析了定位精度的多种影响因素,以提高光纤传感系统的定位准确性。验证结果表明,该系统可实现0.01 m的定位精度,证明了系统的可行性。
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2024年第卷第11期
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doi: 10.20104/j.cnki.1674-6546.20240210
  • 首发时间:2025-11-25
  • 出版时间:2024-11-15
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  • 修回日期:2024-08-27
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    1 安徽理工大学, 淮南 232001
    2 深圳大学, 深圳 518060
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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