SBIR Phase I: Minimal Sensor Signal Processing for Turbine Engine Health Monitoring

该小型企业创新研究(SBIR)第一阶段项目将为GDATS涡流传感器(ECS)开发全波形模型和最小传感器算法，该传感器最近被选为JSF开发的EMD阶段。这些算法将使涡轮发动机的实际实时高性能健康监测成为可能。目前的处理技术可能需要四个或更多传感器；然而，这些方法并没有利用ECS提供的所有信息。使用完整的ECS信号，理论上，只使用一个传感器就可以估计出整体振动频率、相位和幅度。减少每个发动机阶段所需的传感器数量可能会节省数百万美元。目前还没有商业上可用的系统来持续监测飞机燃气轮机发动机的健康状况。用于连续健康监测的最小传感器算法有很大的市场。客户包括美国空军、美国海军和英国国防部。这样的系统不仅预计将安装在所有新的涡轮发动机上，例如JSF，而且它们也将被改装到老式飞机上。

This Small Business Innovation Research (SBIR) Phase I project will develop full waveform models and minimal sensor algorithms for the GDATS eddy current sensor (ECS), recently chosen for the EMD phase of the JSF development. These algorithms will enable the practical real-time high performance health monitoring for turbine engines. Current processing techniques could require four or more sensors; however, these approaches do not make use of all the information made available by the ECS. Using the full ECS signature, it is possible, in theory, to estimate integral vibration frequency, phase and amplitude using only a single sensor. The reduction of the number of sensors required in each engine stage could potentially save millions of dollars.There are no systems commercially available today for continuous health monitoring of aircraft gas turbine engines. The minimal sensor algorithms for continuous health monitoring have a large market. The customers are USAF, USN and UK MOD. Not only are such systems expected to be put on all new turbine engines such as for the JSF, but they will be retrofitted to the older aircraft as well.