摘要
泄漏特别是小漏预警对热力管道的安全维护具有重要意义。受空间分辨率的影响,分布式光纤传感器对小漏引起的局部温度变化测试精度较低,测量温度与实际温度差异较大。以布里渊光时域反射仪(BOTDR)作为测量手段,提出了一种建立分布式光纤测量温度与实际温度之间对应关系的方法。设计完成了小漏温度场模拟测量实验,通过高斯拟合对测量数据进行特征提取,再用人工神经网络建立测量温度与实际温度的映射模型。结果表明:设计的实验方案可获得代表管道小漏温度分布的先验数据,基于此训练的人工神经网络可确立实际温度场与BOTDR测量温度场的对应关系,提高了光纤测试精度并为泄漏预警策略的制定提供了依据。
Early warning of leakage, especially small leakage, is significant for safety maintenance of thermal pipeline. Due to spatial resolution, the measuring accuracy of distributed fiber optic sensor for local temperature variation caused by small leakage is low and the measurements are quite different from the actual temperature field. Based on Brillouin optical time domain reflectometer （BOTDR）, a new method to establish a mapping relationship between the BOTDR measurements and the actual temperatures is proposed. Laboratory experiments were carried out to simulate small leakage and achieve the measurements of gradient temperature fields. Feature extraction of the measured data is then conducted through Gaussian fitting. With artificial neural network （ANN）, a mapping model of the actual and measured temperature features is established. The results demonstrate that： the designed experiment can accumulate enough prior data to derive an ANN model, based on which a mapping relation of the actual temperature field and the BOTDR measurements can be achieved to improve the measuring accuracy of BOTDR and provide a reference to propose warning strategy.
出处
《土木建筑与环境工程》
CSCD
北大核心
2016年第2期97-103,共7页
Journal of Civil,Architectural & Environment Engineering
基金
土木工程防灾国家重点实验室自主研究课题(SLDRCE14-B-19)~~
关键词
管道泄漏
布里渊散射
高斯拟合
人工神经网络
pipeline leak
brillouin scattering
gauss fitting
artificial neural network
