MODELING AND CHARACTERIZATION OF CRACK DEPTH ON ROTOR BEARING USING ARTIFICIAL NEURAL NETWORKS

Vibration in rotating machinery is caused by imbalance, misalignment, mechanical looseness, shaft crack, and other malfunctions. The diagnostics of rotor faults have been gaining importance in recent years. In the present study, vibration characteristics of cracked steel shaft were studied. A 25mm diameter and 400 mm length of steel shaft is supported on two bearings at both ends and a 1.2 kg mass of disc is held at the centre of shaft was Studied. Experiments were conducted on cracked steel shaft at different speeds and depth of cracks. Experimental results of amplitude of vibration and frequency were measured at two bearings using an accelerometer in axial, horizontal and vertical directions. Interaction effect of speed and crack depth on the responses was studied using response surface methodology. Predictive models for the responses were developed using Artificial Neural Networks (ANN). The ANN models were training with feed forward back propagation algorithm and the network was used to predict frequency and amplitude of bearing vibration. The predicted values were compared with the collected experimental data and percentage error was computed. The results show that the trained ANN models have better performance to identify the crack location and depth with higher accuracy and efficiency. Further, it can be used in fast identification of crack fault in rotating machinery