Identification of structural damage in flexible structures using system norm and neural networks

• Autor(a) principal: Cordeiro, Leandro [UNESP]
• Data de Publicação: 2006
• Outros Autores: Bueno, Douglas Domingues [UNESP], Marqui, Clayton Rodrigo [UNESP], Lopes Jr., Vicente [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: https://www.sem.org/Proceedings/ConferencePapers-Paper.cfm?ConfPapersPaperID=21645; http://hdl.handle.net/11449/69404
• topic: Damage detection; Damage quantification; Neural nets; System norms; Artificial neural net; Beam-like structures; Black boxes; Computational model; Damage Identification; Damage position; Intelligent method; Maintenance operations; Non-destructive test; Numerical applications; Practical problems; Predictive maintenance; Seven-level; Simultaneous control; Structural damages; Structural repairs; Working life; Data processing; Exhibitions; Flexible structures; Identification (control systems); Maintenance; Neural networks; Nondestructive examination; Structural analysis; Structural dynamics

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.