Abstract
In this paper, a new intelligent portable mechanical system is introduced experimentally and theoretically to detect damage employing the fuzzy-genetic algorithm and EMD. For this purpose, the acceleration-time history is obtained from three points of a simply-supported beam utilizing accelerometer sensors. The gained signal is decomposed into small components by using an EMD method. Each decomposed component contains a specific frequency range. Finally, the proposed algorithm is designed to find the location and severity of damage through the frequency variation pattern among the safe and the damaged beam.
Highlights
Defects and failures in structures, which are detrimental of human life and financial resources, can be eliminated through structural damage identification methods that have been studied previously through the literature
Modal analysis methods are very popular due to practicality. These methods are based on the fact that modal parameters depend on the physical parameters and any changes in physical parameters can lead to a change in modal parameters of the structure [1,2]
Any damage detection system is divided into several sections such as damage identification, identification of damage location, damage rate detection, and prediction of the damaging lifetime
Summary
Defects and failures in structures, which are detrimental of human life and financial resources, can be eliminated through structural damage identification methods that have been studied previously through the literature. A new intelligent portable mechanical system is proposed for damage detection in beam-shaped structures using the fuzzy-genetic algorithm. The advantages of this technique are as follows: -- It could be applied for the mixed type of structures due to the system’s simplicity and efficiency. To convert the acceleration-time history into usable information for damage identification, the EMD method is utilized. In this manuscript, the empirical method of signal decomposition into the main modes is firstly introduced and the capabilities of the method for damage detection are investigated. The dominant frequencies of each IMF are used as features of the FuzzyGenetic Algorithm (FGA) to detect the location and severity of damage in the structure
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