Issue 48

J. Prawin et alii, Frattura ed Integrità Strutturale, 48 (2019) 513-522; DOI: 10.3221/IGF-ESIS.48.49 521 In order to test the robustness of the proposed algorithm with limited measurements experimentally, the measurements of both the single and two crack specimens at optimally chosen four sensors i.e., at nodes 1, 3, 5 and 8 are only considered instead of all the 8 sensors. The damage index is computed using the limited sensor information. The corresponding results are also presented in Fig. 10. It can be observed from Fig. 10 that the damage index exhibit pairwise peaks at nodes 3 and 5 for the single crack specimen where the crack is actually located close to the sensor node 4. It can also be concluded from Fig. 10 that in the case of two-crack specimen, only one crack at sensor node 3 (i.e. the crack is actually located between nodes 2 and 3) is detectable, with limited measurements of 4 sensors. This study clearly illustrate that only he possible region of spatial location of closing crack can be identified with limited instrumentation and it is also difficult to localize multiple cracks. From the numerical and experimental investigations, it is clear that the proposed signal decomposition approach based on singular spectrum analysis can identify more than one cracks present anywhere in the structure and of any crack depth with minimum optimal number of sensors. C ONCLUSIONS signal decomposition based technique has been proposed in this paper to identify the smaller and subtle closing cracks present anywhere in the structure. Both experimental and numerical investigations have been carried out on simple beam-like structures to test the robustness and effectiveness of the proposed signal decomposition based breathing crack localization technique. The results furnished from the investigations are given below 1. The proposed signal decomposition is capable of isolating linear, nonlinear and noise components effectively. 2. The proposed SSA based damage diagnostic technique is robust enough to locate even more than one crack present in the structure. 3. Numerical investigations conclude that the proposed breathing crack localization technique is reference free and can detect minor crack of about 7% crack depth. Experimental investigations conclude that the proposed technique can detect crack depth of about 12.5% of overall depth of the beam. 4. The proposed approach can identify the breathing crack even with the acceleration responses being polluted with 10% measurement noise. 5. The proposed closing crack localization technique can also identify smaller cracks due to consideration of more number of super harmonics in contrast to the earlier works which consider only first few super harmonics. A CKNOWLEDGEMENTS he authors thank the technical staff of ASTAR and SHML Lab of CSIR-SERC for their support during laboratory testing. R EFERENCES [1] Golyandina, N., Nekrutkin, V. and Zhigljavsky, A.A. (2001). Analysis of time series structure: SSA and related techniques. Chapman and Hall/CRC. [2] Vautard, R., Yiou, P. and Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1-4), pp.95-126. [3] Loh, C.H., Chen, C.H. and Hsu, T.Y. (2011). Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam. Structural Health Monitoring, 10(6), pp.587-601. [4] Prawin, J., Lakshmi, K. and Rao, A.R.M. (2018). A novel singular spectrum analysis–based baseline-free approach for fatigue-breathing crack identification. Journal of Intelligent Material Systems and Structures, 29(10), pp.2249-2266. [5] De Oliveira, M.A., Vieira Filho, J., Lopes Jr, V. and Inman, D.J. (2017). A new approach for structural damage detection exploring the singular spectrum analysis. Journal of Intelligent Material Systems and Structures, 28(9), pp.1160-1174. [6] Shen, M.H. and Chu, Y.C. (1992). Vibrations of beams with a fatigue crack. Computers & Structures, 45(1), pp.79-93. A T