Towards Application of Soft Computing in Structural Health Monitoring
The paper presents preliminary results of data analysis and discusses the application of soft computing methods in the field of non-destructive tests. The main objective of developed diagnostic system are the automatic detection and evaluation of damage. Thus the system is composed of two signal processing techniques known as novelty detection and pattern recognition. For this purpose autoassociative as well as feed-forward neural networks are used. All the signals used for training the system are obtained from laboratory tests of strip specimens, where phenomenon of elastic wave propagation in solids was utilized. Computed parameters of time signals defines various types of input vectors used for training neural networks. The results finally obtained prove that the proposed diagnostic system made automation of structure testing possible and can be applied to Structural Health Monitoring.
KeywordsNeural networks novelty detection damage evaluation elastic waves signal processing structural health monitoring
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