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Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第9期14卷 615-630页

作者： Arturo GARCIA-PEREZ Juan P. AMEZQUITA-SANCHEZ Aurelio DOMINGUEZ-GONZALEZ ramin sedaghati Roque OSORNIO-RIOS Rene J. ROMERO-TRONCOSO HSPdigital-CA Telematica-Procesamiento Digital de Se ales DICIS Universidad de Guanajuato Carr. Salamanca-Valle km 3.5+1.8 Palo Blanco 36885 Salamanca Gto. Mexico HSPdigital CA Mecatronica Facultad de Ingeniería Campus San Juan del Río Universidad Autonoma de Queretaro Moctezuma 249 Col. San Cayetano 76807 San Juan del Río Qro. Mexico Department of Mechanical and Industrial Engineering Concordia University1455 de Maisonneuve Boulevard West Montreal QC H3G 1M8 Canada

Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.

关键词： Truss structure Vibration Spectral analysis Wavelet packet transform Empirical mode decomposition Artificialneural network (ANN)

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