Applications of Artificial Neural Network and Wavelet Transform For Condition Monitoring of the Combined Faults of Unbalance and Bearing Clearance
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 7
Abstract
The vibration analysis of rotating machinery indicates 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. 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 the combined faults of unbalance and bearing clearance on the frequency components of vibration signature of the rotating machinery. The experimental data of frequency components and corresponding Root Mean Square (RMS) velocity (amplitude) data are used as inputs to train the ANN, which consists of a three-layered network. The ANN is trained using an improved multilayer feed forward back propagation Levenberg-Marquardt algorithm. In particular, an overall success rates achieved were 99.78% for unbalance, 99.81% bearing clearance, and 99.45% for the combined faults of unbalance and bearing clearance. The wavelet transform approach enables instant to instant observation of different frequency components over the full spectrum. A new technique combining the WT with ANN performs three general tasks data acquisition, feature extraction and fault identification. This method is tested successfully for individual and combined faults of unbalance and bearing clearance at a success rate of 99.99%.
Authors and Affiliations
H. K. Srinivas , Shamanth. S. Holla , Karthik. B. S , Niroop. S , Chiranjeevi. D
Keywords
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