SBIR Phase I: Early Detection of Anomalies in Large-Scale Gas Networks

这项小型企业创新研究（SBIR）项目的更广泛影响/商业潜力是在未来3到5年内大幅减少全国天然气管道故障的发生率。每年都有几百起“重大”管道事故（死亡或重大财产损失）造成巨大的生命和财产损失，分散危险物质并扰乱天然气分配服务。这些事件导致数亿美元的修复和恢复费用，以及高达10亿美元或更多的巨额罚款。这种可伸缩且经济的功能将显著降低此类故障的可能性，而不需要额外的基础设施。性能已经在一家大型公用事业公司得到验证，并且原型已经证明能够捕获大量以前未检测到的事件，并提供重要的提前警告（90分钟或更长时间）。这一结果表明，与现有系统相比，该系统的性能有了明显的提高，并通过为天然气公用事业领域定制的先进模型得以实现。本项目开发的方法可以直接应用于其他环境，如电网网络、计算机集群管理和金融欺诈检测，以提高检测精度。SBIR一期项目提出，通过对连续观测到的压力、普遍温度和网络其他特征的时间序列数据进行统计推断，来检测大型天然气公用事业网络中的异常情况。由于各种原因，例如管道中的硫或冰积聚，以及硬件的腐蚀/老化，天然气公用事业网络中的异常情况会发生，并且通常在气体压力数据的时间序列中可以检测到信号。该项目的前提是早期检测到这些信号，提前90分钟或更长时间发出预警，以便在公用事业网络内采取纠正措施，避免重大财产损失、生命损失和服务中断。该项目提出了从大量流数据中快速估计气体压力行为的短期和中期时间尺度模型的新方法，以及通过蒙特卡罗和随机优化技术构建预测带的方法。这些方法不是通用的，它们的成功关键依赖于利用网络级气体压力时间序列特有的特定结构特性，以及统计机器学习的现代趋势。所提出的随机优化技术将根据概率将已识别的异常分类为“故障类型”，从而允许对网络级紧急操作进行优先排序。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to dramatically reduce the incidence of natural gas pipeline failures across the country, within the next 3 to 5 years. Every year there are a few hundred "significant" pipeline accidents (fatalities or significant property damage) causing massive damage to life and property, dispersing hazardous materials and disrupting gas distribution services. These events result in many hundreds of millions of dollars in repair and recovery, and large fines that can be up to a billion dollars or more. This scalable and economical capability will significantly reduce the likelihood of such failures, without requiring additional infrastructure. Performance has been validated at a large utility company, and the prototype has demonstrated the ability to capture a substantial fraction of previously undetected events with significant advance warning (90 minutes or more). This outcome represents a clear performance improvement over existing systems and is enabled by advanced models customized for the gas-utility domain. The methods developed in this project can be directly applied to improve detection accuracy in other contexts such as power-grid networks, computer cluster management and financial fraud detection. This SBIR Phase I project proposes to detect anomalies in large-scale gas-utility networks through statistical inference from continuously observed time-series data on pressure, prevailing temperature, and other characteristics of the network. Anomalies within gas-utility networks occur due to a variety of reasons, e.g., sulphur or ice buildup in the pipelines, and corrosion/aging of hardware, and are often preceded by detectable signatures in the time-series of gas-pressure data. A premise of the project is that the early detection of such signatures, leading to advance warning of 90 minutes or more, allows corrective action within the utility network to avoid significant property damage, loss of life, and service disruption. The project proposes new methods for the rapid estimation of short and medium timescale models of gas pressure behavior from voluminous streaming data, along with methods for constructing prediction bands through Monte Carlo and stochastic optimization techniques. Such methods are non-generic and their success relies crucially on exploiting specific structural properties that are unique to network-level gas-pressure time series, along with modern trends in statistical machine learning. The proposed stochastic optimization techniques will probabilistically classify identified anomalies into ``failure type," allowing the prioritizing of network level emergency operations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.