SBIR Phase II: Proposal for Research into Low-Cost Distributed Wireless Sensing of Operational Condition in Industrial Electric Motors

这个小型企业创新研究（SBIR）第二阶段项目将产生一个商业上可行的低成本无线传感器网络，可以在故障发生之前预测电机故障。 美国的工业和制造商严重依赖电动机为其设备和工艺提供动力。 为了最大限度地减少电机停机成本，许多人使用劳动密集型预防性维护计划，按固定时间表手动检查电机。这些检查平均每台电机每年花费500美元，其中80%的成本被浪费。 通过使用24 x7真实的监控电机性能的传感器，可以消除这种浪费。 不幸的是，目前的系统平均每个电机花费数千美元。 这对于占电机市场98%的大多数1-150 HP电机应用来说太昂贵了。 这项研究将量化和完善的低成本网络的传感器节点的性能，和算法所使用的节点预测电机故障，通过受控的实验室测试和现场测试。 它还将把振动能量收集技术集成到节点中。 其结果将是一个传感器原型网络，证明符合关键的性能和价格指标，并在商业上可行的1-150马力motors.The更广泛的影响/商业潜力，该项目的原型低成本传感器系统将促进预测维护的电动机在美国的工业和制造设施的一小部分，目前的替代品的成本。 因此，美国各地的数万个设施将能够负担得起对其电机系统实施预测性维护的初始投资，最大限度地延长正常运行时间，并最大限度地降低电机维护成本。 这将提高这些美国工业和制造业公司的竞争力，最终有助于创造和保护美国的就业机会。 此外，作为研究结果产生的原型系统将为低成本，低功耗的无线传感器节点提供重要的概念验证，这将有助于刺激该领域的未来发展和投资，这反过来又有助于“智能电网”，“智能城市”和其他智能基础设施的发展。

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.