SBIR-STTR Award

This SBIR Phase I research proposal proposes research to develop an ultra-compact, low cost system for wirelessly monitoring motors that will cost manufacturers less than $300/ motor to implement. The research will leverage existing patent-pending energy-efficient algorithms for determining motor condition based on vibration and temperature data to develop wireless nodes capable of autonomously determining the condition of any motor to which they are attached. The research will result in a prototype system of wireless nodes implemented at an industrial partner, which will provide the necessary incentive for future investment in the company and technology. The broader impact of this research will be to enable a wireless system to facilitate condition-based maintenance of electric motors in industrial facilities at a cost of less than $300 per motor to manufacturers, which is about 10% of the cost of current systems. At this price point, tens of thousands of facilities around the United States will be able to afford the initial investment to implement condition-based maintenance on their motor systems. Since condition-based maintenance has been shown to maximize up-time and minimize yearly maintenance costs, this will increase the competitiveness of American manufacturing and ultimately help create more manufacturing sector jobs. Additionally, the prototype system produced as a result of the research will provide an important proof-of-concept for low-cost, low-power wireless sensor nodes that should help spur future development and investment in this field, which is in turn instrumental for the development of "smart grids", "smart cities", and other intelligent infrastructure.

This Small Business Innovation Research (SBIR) Phase II project will result in a commercially viable network of low cost wireless sensors that predict electric motor failure before failure occurs. American industries and manufacturers rely heavily on electric motors to power their equipment and processes. To minimize motor downtime costs, many use labor intensive preventative maintenance programs, manually inspecting motors on a fixed schedule. These inspections cost an average of $500 per motor per year, with 80% of that cost wasted. This waste can be eliminated through the use of sensors that monitor motor performance in real time, 24x7. Unfortunately, current systems cost thousands of dollars per motor on average. This is too expensive for most 1-150 HP motor applications, which comprise 98% of the motor market. This research will quantify and refine the performance of a low cost network of sensor nodes, and algorithms used by the nodes to predict motor failures, through controlled laboratory testing and field testing. It will also integrate vibration energy harvesting technology into the nodes. The result will be a network of sensor prototypes that are demonstrated to meet key performance and price metrics, and are commercially viable for use with 1-150 HP motors. The broader impact/commercial potential of this project is that a prototype low cost sensor system will facilitate predictive maintenance of electric motors in US industrial and manufacturing facilities at a fraction of the cost of the current alternatives. As a result, tens of thousands of facilities around the US will be able to afford the initial investment to implement predictive maintenance on their motor systems, maximize up-time, and minimize motor maintenance costs. This will increase the competitiveness of these US industrial and manufacturing firms and ultimately help create and preserve American jobs. Additionally, the prototype system produced as a result of the research will provide an important proof-of-concept for low-cost, low-power wireless sensor nodes that should help spur future development and investment in this field, which is in turn instrumental for the development of "smart grids", "smart cities", and other intelligent infrastructure.