Application of Neural network for fault diagnosis of cracked cantilever beam

Abstract

This paper discusses neural network technique for fault diagnosis of a cracked cantilever beam. In the neural network system there are six input parameters and two output parameters. The input parameters to the neural network are relative deviation of first three natural frequencies and first three mode shapes. The output parameters of the neural network system are relative crack depth and relative crack location. To calculate the effect of crack depths and crack locations on natural frequencies and mode shapes, theoretical expressions have been developed. Strain energy release rate at the crack section of the beam has been used for calculating the local stiff nesses of the beam. The local stiff nesses are dependent on the crack depth. Different boundary conditions are outlined which take into account the crack location. Several training patterns are derived and the Neural Network has been designed accordingly. Experimental setup has been developed for verifying the robustness of the developed neural network. The developed neural network system can predict the location and depth of the crack in a close proximity to the real results.