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

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是在未来3到5年内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输送服务。此类事件可能对公用事业公司造成严重后果，包括破产、数十亿美元的债务、民事/刑事处罚和更高的保险费。这种在灾难性故障之前主动识别潜在问题的可扩展且经济的能力将显著降低此类故障的可能性，而无需额外的基础设施。该项目还将有助于防止管道附近未经授权的第三方活动，这是导致事故的主要原因。本项目开发的方法可以直接应用于其他环境，如电网、计算机集群管理和金融欺诈检测，以提高检测精度。SBIR二期项目提出通过对压力、温度和网络特征的连续观测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。天然气公用事业网络出现异常的原因有很多，比如硫或结冰、调节器故障、硬件腐蚀/老化以及人为错误。这种故障通常在气体压力数据的时间序列中可以检测到特征。通过显著的提前预警（90分钟或更长时间）及早发现这些信号，可以采取纠正措施，避免生命损失、财产损失和服务中断。该项目提出了从实时管道数据快速估计气体压力行为的中短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。这些方法都是非通用的，它们的成功关键依赖于利用网络级气压时间序列特有的特定结构特性。所提出的随机优化技术将对已识别的异常进行概率分类，从而允许对网络级应急操作进行优先级排序。该项目还将开发可扩展的自动化高维分类模型，以从卫星和其他图像数据中检测建筑活动，从而启动预防措施。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) Phase II project is to 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 delivery services. Such events can have severe consequences for a utility including bankruptcy, billions of dollars in liabilities, civil/criminal penalties and higher insurance premiums. This proposed scalable and economical capability to proactively identify potential issues prior to catastrophic failure will significantly reduce the likelihood of such failures without requiring additional infrastructure. The project will also help prevent unauthorized third-party activity near pipelines, a leading cause of accidents. The methods developed in this project can be directly applied to improve detection accuracy in other contexts such as power-grids, computer cluster management and financial fraud detection.This SBIR Phase II project proposes to detect anomalies in large-scale gas-utility networks through statistical inference from continuously observed time-series data on pressure, temperature, and network characteristics. Anomalies within gas-utility networks occur for various reasons, such as sulphur or ice buildup, regulator malfunction, corrosion/aging of hardware, and human error. Such failures are often preceded by detectable signatures in the time-series of gas-pressure data. Early detection of such signatures with significant advance warning (90 minutes or more) allows corrective action that will avoid loss of life, property damage and service disruption. This project proposes new methods for the rapid estimation of short and medium timescale models of gas pressure behavior from real-time pipeline 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 unique to network-level gas-pressure time series. The proposed stochastic optimization techniques will probabilistically classify identified anomalies into failure type, allowing the prioritizing of network level emergency operations. The project will also develop scalable automated high-dimensional classification models to detect construction activity from satellite and other image data, to initiate preventive action.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.