Crack detection in beam-like structures using genetic algorithms

Authors:
Mohammad-Taghi Vakil-Baghmisheh;Mansour Peimani;Morteza Homayoun Sadeghi;Mir Mohammad Ettefagh
Affiliations:
Research Laboratory of Intelligent Systems, Faculty of Electrical Engineering, University of Tabriz, Tabriz, Iran;Research Laboratory of Intelligent Systems, Faculty of Electrical Engineering, University of Tabriz, Tabriz, Iran;Research Laboratory of Vibration and Modal Analysis, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran;Research Laboratory of Vibration and Modal Analysis, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Venue:
Applied Soft Computing
Year:
2008

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Abstract

A fault diagnosis method based on genetic algorithms (GAs) and a model of damaged (cracked) structure is proposed. For modeling the cracked-beam structure an analytical model of a cracked cantilever beam is utilized and natural frequencies are obtained through numerical methods. Our method utilizes genetic algorithms to monitor the possible changes in the natural frequencies of the structure. The identification of the crack location and depth in the cantilever beam is formulated as an optimization problem, and binary and continuous genetic algorithms (BGA, CGA) are used to find the optimal location and depth by minimizing the cost function which is based on the difference of measured and calculated natural frequencies. Also we present a new cost function based on natural frequencies. The average values of location and depth prediction errors are 1.02% and 1.98%, respectively, using the BGA. These values become 0.73% and 1.11% for the CGA. To validate the proposed method and investigate the modeling and measurement errors some experimental results are also included. The average values of experimental location and depth prediction errors are 10.57% and 11.19%, respectively, for the BGA. These values become 10.21% and 10.39% for the CGA.