Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain

Three-phase induction motors are widely used in different applications the industry due to their robustness, low cost, and reliability. Untimely identification correct diagnosis of incipient faults reduce cost improve maintenance management these machines. This paper explores a new method for robust classification rotor failures three-phase (MITs) connected directly electrical network, operating steady-state, under unbalanced voltages load conditions. Through an innovative methodology, analysis current signals from 1 hp 2 frequency domain was performed. Such applied constructing input matrices Multilayer Perceptron Neural Network (MLPNN) detect faults. Furthermore, this methodology proved be because samples failing healthy include voltage unbalance conditions supply significant variation motor shaft. detection 1, 2, 4 broken bars consecutively on condition other diametrically opposite. The results were promising obtained using 847 real experimental bench construct neural model its respective validation.