Assessment and Localization of Active Discontinuities Using Energy Distribution Between Intrinsic Modes

Ghazi, Reza Mohammadi; Büyüköztürk, Oral

doi:10.1007/978-3-319-04570-2_1

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Kurtosis can also be used as a measure of peakedness of the distributions which was used in [30] as the only DI. The sign of the argument of the absolute value function in the numerator of DI 1 and DI 4 can also be used for a rough severity assessment [31]. …”

Section: Dis From the Original Energy Distributionsmentioning

confidence: 99%

Damage detection with small data set using energy-based nonlinear features

Ghazi

Büyüköztürk

2015

Struct. Control Health Monit.

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This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy-based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection the proposed algorithm combines multiple damage sensitive features through a distance-based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance-based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm is experimentally validated by testing a steel laboratory prototype and the results show that the proposed method can effectively detect and localize the defects. KeywordsEnergy method, hypothesis testing, marginal Hilbert spectrum, normalized cumulative energy distribution, Mahalanobis distance, white noise excitation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 IntroductionVibration based structural health monitoring (SHM) is a widely used method for monitoring large scale, complex structures. Aging of infrastructures, higher operational demands, and variety of environmental effects on structural systems are the main reasons that attract more attention to this field in recent years.The algorithms for vibration-based SHM are either model-based or data-based. Both methods compare the response of the system with a baseline. In model-based approach, the baseline is provided using numerical models. Thus, this method is helpful for systems for which the model already exists and in cases where it is justifiable to build a sufficiently accurate structural model [1][2][3]. The data-based approach brings more flexibility to the damage detection scheme since it only uses the sensor data without having to deal with the complications of creating a model. The initial phase in both of these methodologies is feature extraction. In this phase certain damage sensitive features, called damage index (DI), are extracted from the structure's response, either empirically obtained or numerically simulated, to measure its discrepancies from the response in the intact state. Previous studies show that the features which capture nonlinearities in the structural response are generally more sensitive to damage, less sensitive to environmental conditions, and hence, more reliable for the purpose of damage detection compared to the DIs that capture linear phenomena such as modal properties [4][5][6][7].Note that the source of nonlinearities can be material, geometry, or nonlinear dynamics phenomenon such as dispersion, mode mixing, and damping. The fractal dimension of the attractor of time-series is the basis for defining DIs in [8][9][10][11]. Fractal analysis of residual crack patterns in reinforced concrete struct...

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Section: Dis From the Original Energy Distributionsmentioning

confidence: 99%

Damage detection with small data set using energy-based nonlinear features

Ghazi

Büyüköztürk

2015

Struct. Control Health Monit.

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“…While in Roveri and Carcaterra, 21 the damage location is revealed by the position of a sharp crest of the first instantaneous frequency obtained from the Hilbert transform of the IMFs, in Kunwar et al., 22 the Hilbert–Huang transform spectrum is qualitatively analyzed to identify damage. In Ghazi and Buyukozturk, 23 the energy content of IMFs and also the shape of energy distribution between these modes before and after damage are used for localisation and severity assessment of the damages. In Aied et al., 24 the ensemble EMD is applied to the bridge model midspan acceleration signal to identify a sudden stiffness change; the latter is modelled using a bi-linear hysteretic moment–curvature relationship.…”

Section: Introductionmentioning

confidence: 99%

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

Lofrano

Romeo

Paolone

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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A structural damage identification technique hinged on the combination of orthogonal empirical mode decomposition and modal analysis is proposed. The output-only technique is based on the comparison between pre- and post-damage free structural vibrations signals. The latter are either kinematic (displacements, velocities or accelerations) or deformation measures (strains or curvatures). The response data are decomposed by means of the orthogonal empirical mode decomposition to derive a finite set of orthogonal intrinsic mode functions; the latter are used as a multi-frequency and data-driven basis to build pseudo-modal shapes. A new damage index, the so-called pseudo-mode index, is introduced to compare the response obtained for the two states of the structural system and detect potential damages. The performance of the devised index in detecting a localised damage is shown through numerical and experimental tests on two structural models, namely a 4-degrees-of-freedom system and a two-hinged parabolic arch.

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Bayesian Inference for Damage Detection in Unsupervised Structural Health Monitoring

Mohammadi-Ghazi

Büyüköztürk

2015

Model Validation and Uncertainty Quantification, Volume 3

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Assessment and Localization of Active Discontinuities Using Energy Distribution Between Intrinsic Modes

Cited by 4 publications

References 9 publications

Damage detection with small data set using energy-based nonlinear features

Damage detection with small data set using energy-based nonlinear features

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

Bayesian Inference for Damage Detection in Unsupervised Structural Health Monitoring

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