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

SBIR 第一阶段：大规模天然气网络异常的早期检测

• 批准号: 1820488
• 负责人: Krishna Karambakkam
• 金额: $ 22.44万
• 依托单位: EigenPatterns Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820488&HistoricalAwards=false
• 关键词: SBIR; Phase; Early; Detection; Anomalies

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.

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是在未来3到5年内大大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），造成生命和财产的巨大损失，分散危险材料并中断天然气分配服务。这些事件导致数亿美元的维修和恢复，以及高达10亿美元或更多的巨额罚款。这种可扩展且经济的功能将显著降低此类故障的可能性，而无需额外的基础设施。性能已在一家大型公用事业公司得到验证，原型已证明能够捕获相当大一部分以前未检测到的事件，并具有显著的提前警告（90分钟或更长时间）。这一结果代表了现有系统的明显性能改进，并通过为燃气公用事业领域定制的高级模型实现。在这个项目中开发的方法可以直接应用于提高检测精度在其他情况下，如电网网络，计算机集群管理和金融欺诈检测。该SBIR第一阶段项目提出通过从连续观察到的压力、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燃气公用事业网络中的异常。天然气公用事业网络内的异常由于各种原因而发生，例如，管道中的硫或冰积累以及硬件的腐蚀/老化，并且通常在气体压力数据的时间序列中具有可检测的特征。该项目的一个前提是，早期检测到这种特征，导致提前90分钟或更长时间的警告，允许在公用事业网络内采取纠正措施，以避免重大财产损失，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，沿着提出了通过蒙特卡洛和随机优化技术构建预测带的方法。这些方法是非通用的，它们的成功关键取决于利用网络级气体压力时间序列特有的特定结构特性，沿着统计机器学习的现代趋势。建议的随机优化技术将概率分类到“故障类型”识别的异常，允许网络级紧急操作的优先级。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。