管道泄漏分布式光纤监测时-空大数据分析方法

分布式光纤传感器能够灵敏地感知管道任意位置泄漏导致的局部温度变化,但是海量监测数据却具有显著的时-空非平稳性特征,难以直接根据监测数据进行泄漏诊断。在统计模式识别框架下,提出一种基于滑动窗口离群值分析的时-空大数据分析方法,仅利用分布式温度监测数据的内秉特征即可实现管道泄漏的智能化识别,确定了滑动窗口长度和异常状态诊断窗口长度的取值方法,并且进行了原型保温钢管泄漏监测的物理模拟。结果表明:在管道完好的状态下,该方法不会发生误报警的情况,而管道一旦发生泄漏,该方法能够快速识别管道泄漏事件,并对泄漏位置进行精准定位。该方法是一种无监督的人工智能大数据处理方法,在埋地管道泄漏监测中具有良好的应用前景。

A spatial-time big data analysis method based on distributed fiber optic sensing data for monitoring pipeline leakage

The distributed fiber optic sensor can sensitively monitor the local temperature changes caused by the leakage at any position of the pipeline. The mass monitoring data has obvious spatial-time nonstationary properties, so it is difficult to diagnose the leakage directly from the monitoring data. In this paper, a spatial-time big data analysis method based on moving-window outlier analysis was developed in the framework of statistical pattern recognition. By virtue of this method, the intelligent identification of pipeline leakage is realized only based on the intrinsic characteristic of the distributed temperature sensing data. Then, the method to determine the lengths of the moving window and the abnormal state diagnosis window was proposed. Finally, the physical simulation was performed on the leakage detection of prototype pre-insulated steel pipe. It is indicated that the method developed in this paper can never make the false alarm when the pipeline is in the intact state, and it can quickly identify the leakage event and accurately locate the leakage once a leakage happens. In conclusion, this method is an unsupervised artificial intelligence approach of big data analysis, and its application prospect in the leakage detection of buried pipelines is promising.