Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, have power quality issues such as sag, swell, harmonics, and transients. Thus, a system should the ability detect various faults, even in presence issues. Most fault diagnosis methods proposed earlier misidentified faults caused fail because misclassification due quality. The method uses data along with starting current identify broken rotor bar bearing motors. discrete wavelet transform (DWT) used decompose waveform, then different features mean, standard deviation, entropy, norm are calculated. neural network (NN) classifier for classifying analyzing classification accuracy cases. 96.7% while considering issues, whereas typical case, 93.3%. methodology suitable hardware implementation, which merges consideration trained NN proves stable detection faults.