Department of Statistics, Colorado State University, Fort Collins, Colorado, United States of America.
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
PLoS One. 2019 Feb 13;14(2):e0212287. doi: 10.1371/journal.pone.0212287. eCollection 2019.
The data collected by mobile methane (CH4) sensors can be used to find natural gas (NG) leaks in urban distribution systems. Extracting actionable insights from the large volumes of data collected by these sensors requires several data processing steps. While these survey platforms are commercially available, the associated data processing software largely constitute a black box due to their proprietary nature. In this paper we describe a step-by-step algorithm for developing leak indications using data from mobile CH4 surveys, providing an under-the-hood look at the choices and challenges associated with data analysis. We also describe how our algorithm has evolved over time, and the data-driven insights that have prompted these changes. Applying our algorithm to data collected in 15 cities produced more than 6100 leak indications and estimates of the leaks' size. We use these results to characterize the distribution of leak sizes in local NG distribution systems. Mobile surveys are already an effective and necessary tool for managing NG distribution systems, but improvements in the technology and software will continue to increase its value.
移动甲烷 (CH4) 传感器收集的数据可用于查找城市配气系统中的天然气 (NG) 泄漏。要从这些传感器收集的大量数据中提取可操作的见解，需要经过几个数据处理步骤。虽然这些调查平台已经商业化，但由于其专有性质，相关的数据处理软件在很大程度上构成了一个黑匣子。在本文中，我们描述了一种使用移动 CH4 调查数据开发泄漏指示的分步算法，深入了解与数据分析相关的选择和挑战。我们还描述了我们的算法是如何随着时间的推移而发展的，以及促使这些变化的数据驱动的见解。将我们的算法应用于在 15 个城市收集的数据，产生了超过 6100 个泄漏指示和泄漏大小的估计。我们使用这些结果来描述当地 NG 分配系统中泄漏大小的分布。移动调查已经是管理 NG 分配系统的有效且必要的工具，但技术和软件的改进将继续增加其价值。
