Application of Artificial Neural Network and Wavelet Transform for Vibration Analysis of Combined Faults of Unbalances and Shaft Bow

The vibration analysis of rotating machinery can give an indication of the condition of potential faults such as unbalance, bent shaft, shaft crack, bearing clearance, rotor rub, misalignment, looseness, oil whirl and whip and other malfunctions. The diagnostics of rotor faults has gained importance in recent years. Many papers in the literature have dealt with single faults but normally, more than one fault can occur in a rotor. This paper describes the application of artificial Neural Network (ANN) and Wavelet Transform (WT) for the prediction of the effect of combined faults of unbalance and shaft bow on the frequency components of vibration signature of the rotating machinery. The characteristic features of frequency domain vibration signals have been used as inputs to the ANN consisting of one input, one hidden and one-output layers. The ANN is trained using multiplayer feed forward back propagation marquardt algorithm. It is found that marquardt algorithm is much more efficient than the other techniques. The ANN used for diagnosis and quantifying of faults. The success 160 H. K. Srinivas, K. S. Srinivasan and K. N. Umesh rates based upon each fault have been reported .In particular, an over all success rates of unbalance 99.78%, shaft bow 99.81% and combined faults of unbalance and shaft bow 99.45% has been achieved. The wavelet transform approach enables instant to instant observation of different frequency components over the full spectrum. A new technique combining the (WT) and (ANN) for detection of faults in three phases. This method is tested successfully for individual and combined faults of unbalance and shaft bow at a rate of 99.9%.