Investigation of Crack in Beam Structure using an Adaptive-Genetic Algorithm (AGA)
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Fault detection and continuous condition monitoring in structural and machine elements are very sensitive topics and gaining significant value as a current research area. Due to the continuous loading and unloading of these elements, fatigue occurs. For the above-mentioned reason, crack is initiated and propagated. The initiation of any type of crack changes the physical properties of the structural and machine elements, which directly affects the lifetime of the element. The presence of any discontinuity changes the physical properties of the element, which also changes elastic properties. These alterations in physical properties change the modal properties of the structural elements. These changes in the vibration criteria can be used for the identification and quantification of the damage. In this research work, the vibration parameters are combined with Artificial Intelligence (AI) to predict the damage location. Here the natural evolution-based Genetic Algorithm (GA) has been used for the training of vibration features (frequencies). It has been discovered that the original AI methods are sometimes not able to give the proper prediction of damage location as they may be trapped in the local optimum. So, to counteract this loophole and to make it more flexible so that it can adjust to the constraints of real-life problems, a data mining method using Regression Analysis (RA) has been proposed and the results have been compared.
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