Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

Three-phase induction motors are the primary means of transformation of electrical energy into mechanical energy in industry, since they are robust and present low cost. However, despite being robust, these machines are subject to electrical or mechanical faults. Thus, identifying a defect in a running motor may decrease the risk of possible damage. This paper proposes an alternative approach to identify defects in the stator of these motors, by analyzing current signals in the time domain. In addition, it presents the determination of the consequent fault severity by means of two proposals: 1) a multiagent system with a classifier behavior; and 2) a neural estimator. The faults observed are related to short circuits between turns in the stator coil of 1% to 10%. Experimental results are observed with motors of different powers, under various adverse situations of electrical feed and a wide range of load conditions on the machine shaft.