Non-intrusive radio frequency polarimetry measurements of rotor whirl using artificial neural networks

Fault diagnosis in rotating machinery, especially in aviation, is an active research area. RF sensors have the potential to provide on-wing information for fault diagnosis for gas turbine engines. A dual-polarized, distributed RF sensor system was tested in an axial compressor to determine if the measurement system could detect and predict rotor offset and whirl. A magnetic bearing system was used to dynamically control the rotor position. The RF polarimetry data were used as the input data to simple, multilayer neural networks. Regression analysis was performed to predict the rotor shaft centerline position and rotor whirl orbits' major and minor axis lengths. Typical neural network error was within 5 mu m for whirl orbits up to 175 mu m. The quality of the neural network prediction as a function of the number of features was studied. The results suggest that a final configuration may only require a single transmit-receive antenna pair and one RF tone.