Autonomous data analytics for pipeline leakage detection

原油管道泄漏检测对于防止污染和减少对环境的破坏至关重要。此外，它还可以降低与维修和油损相关的高成本。原油管道的泄漏通常可以通过测量压力信号来检测。我们与位于艾伯塔省的TOKU工业公司（一家专门设计油气管道监测系统的公司）合作，提出了一种基于更强大、更准确的泄漏模式识别的自主泄漏检测方案。该方案包括能够产生实时警报的机载泄漏检测算法，以及用于提取泄漏模式并随时间变化趋势的离线压力数据模型。该设计的主要考虑因素包括通过减少误报和漏报数量来提高检测精度，以及在便携式智能压力传感器上实现板载检测算法的低计算复杂度。在这项工作中，我们专注于开发易于部署的具有成本效益的软件解决方案。我们提出了一种基于大量历史数据集的泄漏特征数据模型，而不是基于流体力学和实验来构建瞬态模型。

Leakage detection in crude oil pipelines is extremely crucial because it can help prevent pollution and reduce damage to environment. Furthermore it can reduce high cost related to repairs and loss of oil. Leakage in crude oil pipelines can often be detected by measuring pressure signals. Working with TOKU Industry Inc. a company based in Alberta specializing in design of monitoring systems for Oil&Gas pipelines, we propose to develop an autonomous leakage detection scheme based on more robust and accurate leak pattern identification. The scheme consists of an onboard leak detection algorithm which is capable of generating real-time alarms, and an off-line pressure data model used to extract leakage patterns and trend their change over time. Main considerations of the design include improved detection accuracy by reducing the number of false alarms and missed alarms, as well as low computational complexity for the onboard detection algorithm to be implemented on the portable smart pressure sensors. In this work, we focus on developing cost-effective software solutions that can be easily deployed. Instead of constructing transient models based on hydrodynamics and experiments, we propose to construct a data model for leak signatures by exploring a great number of historical datasets.