Prediction of Unbalance in Rotor Using Artificial Intelligence

Balancing of rotors can be seen as time-consuming process when used in large industrial applications. The productivity decreases as the machine component must be brought to rest for balancing of specific components. In current research with the help of high precession Erbessd@ Instruments Rotor Balancing Machine minimization of residual unbalance is proposed. The rotor is balanced to acquire certain data such as amplitude, frequency, phase value, and residual unbalance which is obtained by the data acquisition system. After substituting the input values, the balancing calculator provides the compensating masses to be added at the required phase which balances the rotor. The data is stored for numerous iterations by changing the running parameters, obtaining the displacement at plane 1 and plane 2, and the RMS value. These output parameters are used to find the decision surface by applying AI Techniques such as ANFIS which would thereby, provide the residual unbalance, the amount of compensating mass to be added at what phase angle reducing the time of doing the experiments. Regression equations are also established to determine the changes in the independent variable associated with the changes in the dependent variable.