SBIR-STTR Award

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.

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.