%0 Journal Article
%T Fault Diagnosis of Beam-Like Structure Using Modified Fuzzy Technique
%A Dhirendranath Thatoi
%A Sasanka Choudhury
%A Prabir Kumar Jena Jena
%J Advances in Acoustics and Vibration
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/491510
%X This paper presents a novel hybrid fuzzy logic based artificial intelligence (AI) technique applicable to diagnosis of the crack parameters in a fixed-fixed beam by using the vibration signatures as input. The presence of damage in engineering structures leads to changes in vibration signatures like natural frequency and mode shapes. In the first part of this work, a structure with a failure crack has been analyzed using finite element method (FEM) and retrospective changes in the vibration signatures have been recorded. In the second part of the research work, these deviations in the vibration signatures for the first three mode shapes have been taken as input parameters for a fuzzy logic based controller for calculation of crack location and its severity as output parameters. In the proposed fuzzy controller, hybrid membership functions have been taken. Several fuzzy rules have been identified for prediction of crack depth and location and the results have been compared with finite element analysis. A database of experimental results has also been considered to check the robustness of the fuzzy controller. The results show that predictions for the nondimensional crack location, , deviate ~2.4% from experimental values and for the nondimensional crack depth, , are less than ~£¿2%. 1. Introduction Cracks may lead to premature failure in engineering structures. The severity and location of cracks in structures can be identified using nondestructive tests and as an inverse problem using optimization techniques. In the recent past, artificial intelligence methods are found to more efficient in this regard and various efforts have been made. Shim and Suh [1] presented a method which uses a synthetic artificial intelligence technique, that is, adaptive-network-based fuzzy inference system (ANFIS) solved via a hybrid learning algorithm (the back propagation gradient descent and the least-squares method) and continuous evolutionary algorithms (CEAs) solving single objective optimization problems with a continuous function and continuous search space. Ganguli [2] developed a fuzzy logic system (FLS) for ground based health monitoring of a helicopter rotor blade. The structural damage was modelled as a loss of stiffness at the damaged location that can result from delamination. A fuzzy gain tuner to tune the gain in the positive position feedback control to reduce the initial overshoot while still maintaining quick vibration suppression has been presented by Gu and Song [3]. Jena et al. [4] proposed a differential evolution algorithm for detecting the crack, in
%U http://www.hindawi.com/journals/aav/2014/491510/