STTR Phase I: Edge-Based Oil Condition Monitoring System for Heavy Equipment

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This Small Business Technology Transfer (STTR) Phase I project will develop an intelligent onsite oil condition monitoring system to quickly analyze the health of high-speed rotating and reciprocating machinery. Oil condition monitoring is a new and high-growth market with revenues of $505 million in 2017 and expected growth to $850 million by 2023. The market for onsite oil monitoring is expected to increase from $193 million to $332 million during the same time frame. This project develops a benchtop oil monitoring minimum viable system for the transportation and heavy equipment industry. The system offers not only the current health status of a machine but also prognosis of wear trends and life cycle. The system informs a prescriptive and optimized maintenance strategy for each piece of machinery, extending its life and reliability and preventing catastrophic machine failures. It will lead to significant cost, energy, and time savings with sustainability benefits. The intellectual merit of this project develops a disruptive onsite oil condition system based on wear debris sensing arrays and artificial intelligence-based oil property sensing technology. The major innovations include: 1) a unique inductive pulse sensing array that can detect fine wear debris, 2) microfluidic signal multiplexing technologies to detect fine wear debris, 3) unique big data analysis and signal multiplexing that allow fast data processing to obtain real time health status, and 4) a novel artificial intelligence technique, namely a General Regression Neural Network (GRNN) approach that allows acquiring robust, reliable measurements of many properties rapidly with sensors’ cross sensitivities, but significantly reduces the network’s training time. As a result, the sensing system provides comprehensive, real-time information about oil conditions and the machine health status.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.