SBIR Phase II: Predictive Algorithms for Water Point Failure

这个小企业创新研究（SBIR）第二阶段项目将开发和应用机器学习统计工具到物联网（IoT）输水和水质传感器。这将使预测和抢先响应的水点故障。这些环境服务的弹性取决于可靠和持续的可靠性指标，由资助机构利用这些指标来激励服务提供商的绩效。在许多地方，这些服务提供者是提供清洁水、安全卫生设施和可靠能源的公用事业公司。然而，在一些农村地区，服务提供者的意图与长期衡量的影响之间仍然存在很大差距。实现SBIR第二阶段的核心目标将有助于形成有效和清洁供水的循环。物联网传感器和服务将通过提供可靠和安全的水来解决最关键的公共卫生缺口之一。针对这种环境的物联网解决方案可能有助于解决这些信息不对称问题，并改善决策和响应。然而，考虑到远程和电力受限的环境，以及固定基础设施之间的高度可变性，包括使用年限、材料、管径、电能质量、旋转设备供应商（泵和发电机）、服务和功能，任何物联网解决方案都必须是定制工程，或者通过分析来补偿这些现场复杂性。相反，我们的SBIR II方法是为每种服务类型开发通用的太阳能蜂窝和卫星物联网硬件，并通过基于云的传感器融合和统计学习解决现场复杂性。通过这种方式，我们大大降低了硬件和物流成本，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机器学习的应用，以识别各种设计的水泵群中的非标称服务交付。我们开发了一种与不同固定基础设施兼容的通用电气井眼传感器，并演示了使用同质传感器平台和自适应机器学习后端解决异构客户硬件问题。

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.