EE - Kalman Filtering applied to Rotating Machinery
The goal of this research is to investigate signal processing techniques for "Once per Revolution" monitoring of rotating machinery. The approach to be investigated relies on estimation of the revolutions-per-minute (RPM), and the application of signal processing to enhance markers/signatures to facilitate tracking. Techniques such as Kalman Filtering will likely be utilized to achieve a solution. The research will involve Matlab-based modeling and simulation and subsequent analysis of filtering techniques.
The RF Communications and Sensing Lab is involved in a number of applied research activities, some of which have spawned spin-offs for commercialization. A future area will potentially revolve around monitoring of rotating machinery. The proposed project deals with an important capability to facilitate the monitoring of machinery with high fidelity.