埋地热力管道泄漏土体温度场光纤监测

分布式光纤温度传感器在热力管道泄漏监测中具有较好的应用前景,但受空间分辨率的限制,分布式光纤传感器对泄漏特别是小漏引起的局部温度变化的测量精度较低,使得测量温度场与实际温度场有较大差异。提出了一种建立两种温度场之间对应关系的方法。通过设计实验来模拟实际泄漏情况,采用拉曼光时域反射仪(ROTDR)、热敏电阻(TSR)测量泄漏土体温度场,对两种测量结果提取特征值,采用人工神经网络建立两种温度场之间的映射关系。结果表明,特征值提取方法合理,人工神经网络模型可以建立ROTDR光纤测量温度场与实际温度场之间的定量关系,从数据处理角度提高了分布式光纤传感器的测试精度,并为小漏预警研究提供参考。

Monitoring of soil temperature field due to leakage of buried thermal pipeline using distributed fiber optic sensor

Distributed fiber optic sensor(DFOS) hasadvantage of leak detection in the buried thermal pipeline. However, limited by the spatial resolution, the distributed sensors have low accuracy to detect the local temperature variation caused by small leak, which makes the temperature measurement quite different from the practicaltemperature field. To solve this problem, one kind ofmethod is proposed to establish the correspondence between thesetwo temperature fields. Laboratory experiments and field tests are designed to simulate the actual leakage, where Raman optical time domain reflectometer(ROTDR) and thermistors are employed to measure the temperature. The extracted features based on the mapping relation between the actual temperature field and the DFOS measurements aredetermined using artificial neutral network(ANN). Experimental results show that the proposed ANN model is effective to establish the relation between the actual and measured temperatures. The measurement accuracy of ROTDR is improved from the aspects of data processing which provides valuablereference for small leak warning.