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

该小型企业创新研究(SBIR)第二阶段项目将为通用动力-先进技术系统(GDATS)涡流传感器(ECS)开发全波形模型和最小传感器算法。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的传感器；然而，这些方法并没有利用ESC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，可能会在发动机的使用寿命内节省数百万美元。目前还没有商业上可用的系统来持续监测燃气轮机发动机的健康状况。一旦投入使用，该系统将使飞行员能够立即对严重的发动机健康问题做出反应，从而避免潜在的灾难性发动机故障和生命损失。用于持续健康监测的最小传感器算法在航空业和迅速扩张的电力行业都有很大的市场。

This Small Business Innovative Research (SBIR)Phase II project will develop full waveform models and minimal sensor algorithms for the General Dynamics - Advanced Technology Systems (GDATS) eddy current sensor (ECS). 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 ESC. 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 over the life of the engine.There are no systems commercially available today for continuous health monitoring of gas turbine engines. Once in use, this system will allow pilots to react immediately to critical engine health problems thus avoiding potentially catastrophic engine failures and loss of lives. The minimal sensor algorithms for continuous health monitoring have a large market spanning the aviation industry, as well as the rapidly expanding power industry.