SBIR Phase I: Predictive Algorithms for Water Point Failure

这一小企业创新研究第一阶段项目的更广泛影响/商业潜力是在发展中国家为财政上可持续和负责任的供水服务创造一个市场。改善水、环境卫生和个人卫生对公共卫生的影响是巨大的，有可能预防至少9.1%的全球疾病负担和6.3%的死亡。目前在发展中国家提供水服务的方法通常集中在部署、维护和监测援助项目，但只持续几年。影响通常由执行者（非营利、私人和政府）直接评估。然而，即使衡量到积极影响，这些环境服务和监测干预措施大多数都是短期的，衡量结果可能会产生误导。例如，一个为期数十年的项目显然将农村地区获得清洁水供应的比例从1990年的58%提高到2015年的91%。改进的服务可以通过预防性和“及时”维护活动来实现，通过仪器和预测性故障数据分析算法来实现。这可能至关重要地实现供水零中断。由供水点故障引起的中间获得水，清洁水已知会增加健康风险。这个小企业创新研究（SBIR）第一阶段项目旨在开发预测机器学习算法，用于供水点故障，这些故障来自安装在发展中国家农村供水基础设施上的蜂窝报告电子传感器。该提案中提出的研究创新包括采用强大的机器学习分类技术的集合，使用交叉验证方法来调整模型参数并评估性能，以开发能够提前预测故障的数据自适应系统，以便进行预防性维护，维修或更换。 具体来说，我们将首先研究基于条件的维护。与基于时间的维护相比，基于状态的维护具有若干优点，特别是能够将有限的维护资源分配到需要的地方，而不是均匀地分布维护资源，包括可能不需要的地方。我们提出的第1阶段SBIR重点是使用我们现有的传感器硬件开发水点故障的预测算法，并应用于现有客户。我们的第一阶段SBIR的成功标准是一个预测算法，可以准确地识别接近失败的水点。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in creating a market for financially sustained and accountable water services in developing countries. The impact of improved water, sanitation, and hygiene on public health is significant, and has the potential to prevent at least 9.1% of the global disease burden and 6.3% of all deaths. Present-day approaches for delivering water services in developing countries typically focus on deploying, maintaining, and monitoring aid-projects for only a few years. Impact is nominally evaluated by implementers (non-profit, private and government alike) directly. However, even when a positive impact is measured, the majority of these environmental service and monitoring interventions are short-term, and measurements may be misleading. For example, a multi-decade project apparently increased access to clean water supplies in rural areas from 58% in 1990 to 91% in 2015. Improved services may be realized through preventative and "just in time" maintenance activities, enabled through instrumentation and predictive failure data analysis algorithms. This may, critically, enable zero-interruption in water supply. Intermediate access to water, caused by water point failure, to clean water is known to increase health risks.This Small Business Innovation Research (SBIR) Phase I project intends to develop predicative machine learning algorithms for water point failures derived from cellular reporting electronic sensors installed on rural water infrastructure in developing countries. The innovation proposed for research in this proposal consists of employing an ensemble of robust machine learning classification techniques, using cross-validation methods to tune model parameters and evaluate performance, in order to develop a data-adaptive system capable of predicting failure well enough in advance to allow preventive maintenance, repair or replacement. Specifically, we will first examine condition based maintenance. Condition based maintenance has several advantages over time based maintenance, especially the ability to allocate limited maintenance resources where they are needed, instead of spreading maintenance resources evenly, including where they may not be needed. Our proposed Phase 1 SBIR focuses on developing predictive algorithms for water point failures using our existing sensor hardware and applied to existing customers. Our success criteria for a Phase 1 SBIR is a predictive algorithm that can accurately identify water points in near-failure.