The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors

Induction motors are robust machines that are often exposed to a variety of environmental and operating conditions that can result in a number of failures during their use. One such fault is a short-circuit that starts in a few turns, and quickly extends to other winding sections. Early detection and diagnosis of this type of failure is very important and can prevent the complete motor loss. In this work, a multiple linear regression modelling technique is used in synergy with the genetic algorithm optimization and the analysis of variance methods to obtain models to classify the motor operating in normal and in short-circuit conditions. The proposed method is suitable for application in real industrial plants due to three important features: (i) it uses RMS values of voltages and currents, (ii) only simulation data is required to obtain the MLR classification model and (iii) incipient faults can be identified with high accuracy. Experimental tests carried out over a wide range of machine operation conditions demonstrates the simplicity and effectiveness of the new diagnosis method.