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This paper presents the development of an online electrical fault detection system that uses neural network (NN) modeling of induction motor in vibration spectra. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals for continuous spectra so that the NN model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Based on experimental observations, the effectiveness of the system is demonstrated, while minimizing the impact of false alarms resulting from power supply imbalance, and it is shown that a robust and automatic electrical fault detection system has been produced.