Code of Grey Relational Analysis

Code was created to normalize values of S/N ratios, to calculate the weighted Grey relational coefficient, grade, and rank

Abstract— Studying motor faults and their behavior first requires simulating a healthy squirrel cage induction motor, which is achieved when the output values and the target values are similar. In initial experimental results, the output values did not reach target values. The three target values are stator nominal current, nominal torque, and nominal rotational speed per minute (rpm) on the nameplate of the actual motor. Therefore, the goal of this research was to determine the optimum values for the six input parameters that contribute to the minimum difference between the output and target values. This was conducted using MATLAB SIMULINK and evaluated using the Grey relational analysis. Two simulated motors are used to optimize inputs for getting output values that are very closed to the target values. The output values of the simulated motor were almost identical (98.5-99%) to the values of targets on the nameplate data on the actual motor in the laboratory. Therefore, the output values of the simulated motor allow us to study motor faults experimentally. Another result is that a new equation was developed during this research work.

The Grey Relational Analysis is a method to study the design and analysis of experiments for improving product quality if the parameters optimized are more than two. There are three types of loss functions: 1) “the nominal the best; “the smaller the better;” and “the larger the better.” These loss functions also are used in the Grey Relational Analysis. The quality characteristic “the nominal the best,” occurs whenever the output ‘y’ has a finite target value, usually nonzero, and the quality loss is symmetric on either side of the target. The smallest of the best of the loss function is used in this study because the targets, such as the nameplate parameters, are given. So, this particular loss function is used to optimize the input parameters to make the minimum difference between outputs and targets and to improve the shape of the time domain of output parameters of the dynamic simulation model of a squirrel-cage induction motor that has been developed by Ariunbolor Purvee.

The six parameters were selected because they have the greatest influence on the motor outputs compared to other input parameters. In addition, these parameters were selected because they are capable of being varied within +/-10%, while still obtaining outputs that are almost at the value of the targets.