SBIR Phase II: Predictive Algorithms for Water Point Failure

SBIR 第二阶段：水点故障预测算法

• 批准号: 1738321
• 负责人: Daniel Wilson
• 金额: $ 74.96万
• 依托单位: SweetSense Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738321&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Predictive; Algorithms

This Small Business Innovation Research (SBIR) Phase II project will develop and apply machine learning statistical tools to Internet of Things (IoT) water delivery and water quality sensors. This will enable prediction and preemptive response to water point failures. The resilience of these environmental services is dependent upon credible and continuous indicators of reliability, leveraged by funding agencies to incentivize performance among service providers. In many locations, these service providers are utilities providing access to clean water, safe sanitation, and reliable energy. However, in some rural areas, there remains a significant gap between the intent of service providers and the impacts measured over time. Achieving the SBIR Phase II core objectives will help close the loop on effective and clean water delivery. IoT sensors and services will address one of the most critical public health gaps by enabling delivery of reliable and safe water.IoT solutions for this environment may help address these information asymmetries and enable improved decisions and response. However, given the remote and power constrained environments and the high degree of variability between fixed infrastructure including age, materials, pipe diameters, power quality, rotating equipment vendors (pumps and generators), servicing, and functionality, any IOT solution would have to either be bespoke engineering, or compensate for these site-wise complexities through analytics. Instead, our SBIR II approach is to develop universal, solar powered cellular and satellite IOT hardware for each service type, and addresses site complexities through cloud-based sensor fusion and statistical learning. In this way, we significantly reduce hardware and logistical costs, and provide value to our customers through service delivery analytics. In Phase I, we demonstrated the application of simple sensors and sophisticated machine learning to identify off-nominal service delivery across a cohort of water pumps of various designs. We developed a universal electrical borehole sensor compatible with disparate fixed infrastructure, and we demonstrated solving the problem of heterogeneous customer hardware with a homogeneous sensor platform and adaptive machine learning backend.

这项小型企业创新研究（SBIR）第二阶段项目将开发并将机器学习统计工具应用于物联网（IoT）水和水质传感器。这将使对水点失败的预测和先发制人反应。这些环境服务的弹性取决于可靠和连续的可靠性指标，这是通过资助机构利用的，以激励服务提供商之间的绩效。在许多地点，这些服务提供商是公用事业，可以使用清洁水，安全的卫生和可靠的能源。但是，在某些农村地区，服务提供商的意图与随着时间的推移所测量的影响之间存在显着差距。实现SBIR II期核心目标将有助于关闭有效和清洁水的循环。 物联网传感器和服务将通过实现可靠和安全的水的交付来解决最关键的公共卫生差距之一。对此环境的解决方案可能有助于解决这些信息不对称的问题，并可以改善决策和响应。但是，鉴于固定基础设施（包括年龄，材料，管道直径，电源质量，旋转设备供应商（泵和发电机），服务和功能性）在内的遥远和电力约束环境以及固定基础设施之间的高度可变性，任何物联网解决方案都必须通过分析来定位工程或对这些站点的复杂性进行补偿。取而代之的是，我们的SBIR II方法是为每种服务类型开发通用的，太阳能的蜂窝和卫星IoT硬件，并通过基于云的传感器融合和统计学习来解决站点复杂性。这样，我们会大大降低硬件和后勤成本，并通过服务交付分析为客户提供价值。在第一阶段，我们演示了简单传感器和复杂的机器学习的应用，以识别各种设计的水泵群体的外服务。我们开发了一种与固定固定基础架构兼容的通用电气钻孔传感器，并通过均匀的传感器平台和自适应机器学习后端证明了解决异质客户硬件的问题。

项目成果

Demonstration of Tryptophan-Like Fluorescence Sensor Concepts for Fecal Exposure Detection in Drinking Water in Remote and Resource Constrained Settings

• DOI: 10.3390/su12093768
• 发表时间: 2020-05-01
• 期刊: SUSTAINABILITY
• 影响因子: 3.9
• 作者: Bedell, Emily;Sharpe, Taylor;Thomas, Evan
• 通讯作者: Thomas, Evan

Using Feedback to Improve Accountability in Global Environmental Health and Engineering

利用反馈提高全球环境健康和工程的问责制

• DOI: 10.1021/acs.est.0c04115
• 发表时间: 2021
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Thomas, Evan;Brown, Joe
• 通讯作者: Brown, Joe

The Drought Resilience Impact Platform (DRIP): Improving Water Security Through Actionable Water Management Insights

抗旱影响平台 (DRIP)：通过可行的水管理见解改善水安全

• DOI: 10.3389/fclim.2020.00006
• 发表时间: 2020
• 期刊: Frontiers in Climate
• 作者: Thomas, Evan A.;Kathuni, Styvers;Wilson, Daniel;Muragijimana, Christian;Sharpe, Taylor;Kaberia, Doris;Macharia, Denis;Kebede, Asmelash;Birhane, Petros
• 通讯作者: Birhane, Petros

Improved Drought Resilience Through Continuous Water Service Monitoring and Specialized Institutions—A Longitudinal Analysis of Water Service Delivery Across Motorized Boreholes in Northern Kenya

通过持续供水服务监测和专门机构提高抗旱能力——肯尼亚北部机动钻孔供水服务提供的纵向分析

• DOI: 10.3390/su11113046
• 发表时间: 2019
• 期刊: Sustainability
• 影响因子: 3.9
• 作者: Turman-Bryant, Nick;Nagel, Corey;Stover, Lauren;Muragijimana, Christian;Thomas, Evan
• 通讯作者: Thomas, Evan

A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning

• DOI: 10.1016/j.scitotenv.2021.146486
• 发表时间: 2021-03-24
• 期刊: SCIENCE OF THE TOTAL ENVIRONMENT
• 影响因子: 9.8
• 作者: Thomas, Evan;Wilson, Daniel;Coyle, Jeremy
• 通讯作者: Coyle, Jeremy