On the Use of Attractor Dimension as a Feature in Structural Health Monitoring

Nichols, Jonathan M.; Virgin, Lawrence N.; Todd, Michael D.; Nichols, James D.

doi:10.1006/mssp.2002.1521

Cited by 52 publications

(26 citation statements)

References 38 publications

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“…Typically, a region exists between depopulation and saturation, denoted by (r l , r h ) where the slope remains relatively constant, and it is the region that is of interest. Once the region is chosen by the local slope plot, a line can be fit to the data using least squares and the slope is D [10]. The equivalent approximate expression (8) is…”

Section: Incipient Fault Identification Based On Correlation Dimensionmentioning

confidence: 99%

Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction

Wang

Jiang

2010

Aerospace Science and Technology

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Section: Incipient Fault Identification Based On Correlation Dimensionmentioning

confidence: 99%

Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction

Wang

Jiang

2010

Aerospace Science and Technology

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“…For example, damage detection in nonlinear systems using system augmentation (D'Souza and , identification of damage in an aeroelastic system based on attractor changes (Epureanu and Yin, 2004), enhancing nonlinear dynamics for accurate identification of stiffness loss in a thermo-shielding panel , using attractor dimension as a feature in structural health monitoring (Craig et al, 2000;Nichols et al, 2003b), structural health monitoring through chaotic interrogation (Nichols et al, 2003a) are some of the methods that use vibrational data of a nonlinear system for damage detection. Yin and Epureanu (2005) proposed a bifurcation boundary analysis method as a new nonlinear damage detection tool and used it to track bifurcation boundary changes due to damages over a small region of an aeroelastic panel model.…”

Section: Introductionmentioning

confidence: 99%

Bifurcation boundary analysis as a nonlinear damage detection feature: does it work?

Eftekhari

Bakhtiari-Nejad

Dowell

2011

Journal of Fluids and Structures

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“…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 structures [12], state-space reconstruction using the delaycoordinate method [13,14], considering systems under chaotic excitations [15], sensitivity vector 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 [16,17], Poincare' map based methods [18][19][20][21][22], and nonlinear frequency response function [23][24][25][26] are examples of techniques that use nonlinearities in monitoring structures.…”

Section: Introductionmentioning

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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On the Use of Attractor Dimension as a Feature in Structural Health Monitoring

Cited by 52 publications

References 38 publications

Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction

Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction

Bifurcation boundary analysis as a nonlinear damage detection feature: does it work?

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

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