Broken Rotor Bar Fault Detection and Severity Identification in Squirrel Cage Induction Motor Using Empirical Mode Decomposition and Artificial Neural Networks

The broken rotor bar is the fault that most often occurs in induction motors. This paper proposes a method to identify the broken rotor bar fault using a combination of Empirical Mode Decomposition and Artificial Neural Networks (ANNs). The motor current signal is processed using EMD analysis resulting in the Intrinsic Mode Function (IMF) signal. The zero crossing point of the IMF signal is recorded to obtain the Time Successive between Zero Crossing (TSZC). The Probability Density Function (PDF) of the TZSC is used as the ANN classifier input. The PDF properties of peak, width, and standard deviation are selected as the input variables. Two ANNs were designed as fault detection and severity identification systems. The experimental testing also considers the load level variation. The experiment of the broken rotor bar fault diagnostics shows that the ANN-based fault detection system is able to detect faults with accuracy up to 94.2%. Moreover, the ANN-based severity identification successfully identified 76.09% of the cases. In addition, the experiment on load variations reveals that the fault diagnostic is more effective at higher loads.