Abstract

Unbalance and misalignment are the major causes of vibration in rotating machinery, yet only limited research has been conducted on misalignment. The literature reports that misalignment results in an increase in the vibration at a frequency corresponding to two times the rotating speed (2x responses). The research on misalignment conducted so far has modeled the rotor as two coupled shafts supported on linear and non linear bearings, while misalignment is at the coupler. The results reported to date are inconsistent and the vibration response of a misaligned rotor system is not clearly understood. This dissertation presents a study on the effects of a single shaft misalignment on the dynamic response of a rotor-shaft system. A rotor system supported on two rigid bearings with unbalance and misalignment is modeled using the energy method, and Lagrange formulation is used to establish the equations of motion. The misalignment is modeled through introduction of pre-load and nonlinear shaft stiffness in the direction of pre-load. The model is validated by comparing the natural frequencies predicted using the simulation to the rotor system eigenvalue and the forced response from the simulation is verified using finite element method. A response due to perfectly aligned case is compared with those for parallel and angular misalignments of various magnitudes. Simulations are carried out for a speed range of 0 to 10,000 rpm, and the response of the rotor at the 2x is carefully examined to establish the effects of various misalignment and non-linear parameters on the response. Experiments are conducted using a rig test to compare with analytically predicted trends. Various gas turbine engine data gathered from the field are also used to confirm the vibration pattern predicted by the simulations. The simulated results are finally used to develop an expert system that can identify unbalance and misalignment in a rotor system. The expert system is developed using Neural Network. Two types of Neural Networks are explored, the back-propagation and the Logicon Projection Network. Finally, both networks are modified, trained and tested using simulation data. The Logicon projection network showed superior performance during training, and was chosen over the back-propagation network. The developed expert system is tested using field test data of gas turbine engines to demonstrate its effectiveness.