Online unbalanced rotor fault detection of an IM drive based on both time and frequency domain analyses

Effective maintenance program provides incipient fault detection of rotating machines which reduces interim, unscheduled and excessive maintenance actions. By applying suitable online condition monitoring accompanied with signal processing techniques, machines irregularity can be detected at early stage. Therefore, this paper presents an online unbalanced rotor fault detection of an induction motor (IM). Characteristic features of motor current and vibration signals are analyzed in time domain as a fault diagnosis technique which is a key parameter to the fault threshold. Motor current and vibration signal analyses are also done based on Fast Fourier Transform (FFT), Hilbert Transform (HT) and Envelope Detection (ED) with low pass filter method to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the electrical and mechanical fault detections based on FFT, HT and ED analyses are verified using the captured experimental data while the motor is running under different load conditions. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Techniques are used under normal and unbalanced rotor conditions to a 3-phase, 2 pole, 0.246 KW, 60 Hz, 2950 rpm IM drive.