Fault detection in composite beam structure using an evolutionary algorithm

Abstract

In the field of material science, the search of advanced materials for aerospace application has gained main attention of researchers. Composite materials are being highest demanding material for aerospace structural components as having enhanced properties. Despite superior mechanical performances composites are susceptible to crack formation that can progress silently and eventually lead to catastrophic failure. Crack detection at early stage in Aerospace components is now being a significant matter for continuous investigation. Different conventional structural health condition monitoring methods need manual inspection and are time consuming processes. For early detection of damage Intelligent algorithms are combined with conventional structural health monitoring methods to make the damage detection process more robust and accurate. In current work, damage detection method using GA in support of vibration-based damage detection method has developed and its efficiency has checked. As data gathered in data acquisition process contain noisy data due to some error associate to them data filtration required. Regression Analysis is applied to eliminate some amounts of inconsistencies lies in data. Filtered data again trained in GA and error percentage has calculated.