Study on crack diagnosis for podgy shaft based on neural network

The Rayleigh-Timoshenko beam element based on B-spline wavelet on the interval (BSWI) was constructed for discrete podgy shaft and stiffness disc. The wavelet-based finite element model of rotor system was built up to solve the first three natural frequencies which are functions of relative crack location and depth. The relative crack location, relative crack depth and the first three natural frequencies were employed as the training samples to achieve the neural network for crack diagnosis. Measured natural frequencies were served as inputs of the trained neural network and the relative crack location and depth could be identified. The experimental results verify the validity of the method, which is feasible for practical application to crack diagnosis in podgy rotor system.