Fuzzy-Neuro Controler for Smart Fault Detection of a Beam

This paper addresses the fault detection of a cracked cantilever beam using a hybrid artificial intelligence technique. The hybrid technique used here uses a fuzzy-neuro controller. The fuzzy-neuro controller has two parts. The first part is comprised of the fuzzy controller, and the second part is comprised of the neural controller. The input parameters of the fuzzy controller are relative deviation of the first three natural frequencies and the relative values of the percentage deviation for the first three mode shapes. The output parameters of the fuzzy controller are initial relative crack depth and initial relative crack location. The input parameters of the neural segment of the fuzzy-neurocontroller are relative deviation of the first three natural frequencies and relative values of percentage deviation for the first three mode shapes, along with the initial outputs of the fuzzy controller. The output parameters of the fuzzy-neuro controller are final relative crack depth and final relative crack location. For deriving the fuzzy rules and training patterns of natural frequencies, mode shapes, crack depths and crack locations, theoretical expressions have been developed. Several fuzzy rules and training patterns for the fuzzy controller and neural controller of fuzzy-neuro controller are derived respectively. Experimental set-up has been developed for verifying the robustness of the fuzzy-neuro controller. The results of the developed fuzzy-neuro controller and experimental method are in good agreement.