Rotating machinery is common in any industry. Rotating machinery in the modern era
are designed for higher running speeds, tighter clearances and working under extreme conditions
enhancing efficiency of the system to produce and transmit more power. All these lead to many
rotordynamic challenges. Main cause of vibrations is faults in the rotating systems like
unbalance, looseness, etc. In this paper a method is proposed to identify unbalance and looseness
in rotor bearing system using artificial neural networks (ANN) by two different methods; one is
by statistical features and the second by amplitude in frequency domain. In the first case
statistical features are used to train and test the ANN, and in the second case amplitude in
frequency domain is used to train and test the ANN. Experiments are conducted on the rotor
bearing system running at 40 Hz and vibration data is collected by simulating different
unbalance conditions in the rotor. And also experiments are conducted by creating looseness in
the system by loosening the pedestal bolt. Various statistical features and amplitudes in
frequency domain are extracted separately from this vibration data and are fed to neural network.
It is observed that statistical features are giving good results over frequency domain amplitudes.
ANNs are used to identify the unbalance severity and looseness. These results are useful for
making maintenance decision.