Issue 48

J. Prawin et alii, Frattura ed Integrità Strutturale, 48 (2019) 513-522; DOI: 10.3221/IGF-ESIS.48.49 513 Focused on “Showcasing Structural Integrity Research in India” Damage localization of closing cracks using a signal decomposition technique J. Prawin, A. Rama Mohan Rao CSIR Structural Engineering Research Centre, India prawinpsg@gmail.com, http://orcid.org/0000-0002-7579-025X arm2956@yahoo.com , http://orcid.org/0000-0002-6405-3633 A BSTRACT . Fatigue cracks are a common occurrence in engineering structures subjected to dynamic loading and need to identify at its earliest stage before it leads to catastrophic failure. The presence of fatigue-breathing crack or closing cracks is usually characterised by the presence of sub and super- harmonics in the response of the structure subjected to harmonic excitation. It should be mentioned here that the amplitude of nonlinear harmonics are of very less order in magnitude when compared to linear or excitation component. Further, these nonlinear components often get buried in noise as both are having matched (low) energy levels. The present work attempts to decompose the acceleration time history response using singular spectrum analysis and propose a strategy to extract the nonlinear components from the residual noisy time history component. A new damage index based on these extracted nonlinear features is also proposed for closing crack localization. The effectiveness of the proposed closing crack localization approach is illustrated using detailed numerical studies and validated with lab level experimentation on the simple beam-like structure. It can be concluded from the investigations that the proposed signal decomposition based damage localization technique can detect and locate more than one crack present in the structure. K EYWORDS . Pairwise eigenvalues; Singular spectrum analysis; Closing crack; Damage localization; Nonlinearity; Bilinear. Citation: Prawin, J., Rama Mohan Rao, A., Damage Localization of closing cracks using a signal decomposition technique, Frattura ed Integrità Strutturale, 48 (2019) 513-522. Received: 15.11.2018 Accepted: 22.02.2019 Published: 01.04.2019 Copyright: © 2019 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. I NTRODUCTION ingular Spectrum Analysis (SSA) is a popular multivariate analysis technique widely used for signal decomposition and signal approximation. SSA essentially can be described in four steps namely embedding, Singular Value Decomposition (SVD), grouping and diagonal averaging. The Complete description of the technique can be found in Golyandina et al., [1]. SSA is popularly used in several diverse areas like climate change, geophysical phenomena, mineral processing, seismic data processing, and telecommunication applications [1-3]. SSA has been recently applied in the area of structural health S