Online current and vibration signal monitoring based fault detection of bowed rotor induction motor

Regular condition monitoring of rotating machines using advanced spectrum analysis reduces the unexpected breakdown and excessive maintenance of the machines. If the irregularities are not identified in the early stage, the reliable operation of the machines are affected which may become catastrophic to the operation of the rotating machines. Therefore, this paper presents an online condition monitoring based fault detection of 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 signals are analyzed using Fast Fourier Transform (FFT) and Hilbert Transform (HT) to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the proposed FFT and HT based analysis to predict the fault is verified using experimental data and its rate of success under different load conditions is also recorded. It is found that the HT can more precisely identify the fault using vibration signal as compared to the conventional FFT method. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Spectrum analysis techniques are used under normal and bowed rotor condition to a 3-phase, 2 pole, 1/3 hp, 60 Hz, 2950 rpm IM drive.