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

SBIR 第二阶段：大型天然气网络异常的早期检测

• 批准号: 2025906
• 负责人: Krishna Karambakkam
• 金额: $ 99.99万
• 依托单位: EigenPatterns Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025906&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Early; Detection

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.

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。每年都有数百起“重大”管道事故（死亡或重大财产损失），对生命和财产造成巨大损失，分散危险材料并中断天然气输送服务。此类事件可能会对公用事业造成严重后果，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，这是事故的主要原因。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，计算机集群管理和金融fraud detection.This SBIR第二阶段项目提出了检测异常的大型燃气公用事业网络，通过统计推断从连续观察到的时间序列数据的压力，温度和网络特性。天然气公用事业网络内的异常发生有各种原因，例如硫或冰的积累、调节器故障、硬件的腐蚀/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出现可检测的特征之前。早期检测此类特征并提供显著的提前警告（90分钟或更长时间），可以采取纠正措施，避免生命损失、财产损失和服务中断。该项目提出了根据实时管道数据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。这种方法是非通用的，他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。所提出的随机优化技术将概率分类识别的异常故障类型，允许网络级紧急操作的优先级。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，并采取预防措施。该奖项反映了NSF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。