Damage identification by wavelet analysis

Grabowska, Joanna; Palacz, Magdalena; Krawczuk, Marek

doi:10.1016/j.ymssp.2008.01.003

Cited by 83 publications

(30 citation statements)

References 14 publications

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“…θ g m based on equation (27). The position at ( 1 m + )-th iteration is updated using equation (28).…”

Section: Hybrid Optimization Strategymentioning

confidence: 99%

“…The results of their experiments showed that cracks can be located based on the arrival time of the reflected wave, but that the size of the crack can only be qualitatively estimated. Grabowska et al 28 employed the wavelet transform to decompose a guided wave for damage detection. The study demonstrated that the wavelet decomposed guided wave signal can be used to distinguish different kinds of damages.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian model updating approach for experimental identification of damage in beams using guided waves

2014

Structural Health Monitoring

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A Bayesian approach is proposed to quantitatively identify damages in beam-like structures using experimentally measured guided wave signals. The proposed methodology treats the damage location, length and depth as unknown parameters. Damage identification is achieved by solving an optimization problem, in which a hybrid particle swarm optimization (PSO) algorithm is applied to maximize the probability density function (PDF) of a damage scenario conditional on the measured guided wave signals. Signal envelopes extracted by the Hilbert transform are proposed to minimize the complexity of the optimization problem in order to enhance the robustness and computational efficiency of the damage identification. One of the advantages of the proposed methodology is that instead of only pinpointing the multivariate damage characteristics, the uncertainty associated with the damage identification results is also quantified. This outcome provides essential information for making decisions about the remedial work necessary to repair structural damage. The experimental data consists of guided wave signals measured at a single location of the beams. A number of experimental case studies considering damages of different scenarios are used to demonstrate the success of the proposed Bayesian approach in identifying the damages. The results show that the proposed approach is able to accurately identify damages, even when the extent of the damage is small. KeywordsBayesian approach, guided waves, beam, damage identification, probability density function IntroductionInfrastructure plays an important role in our daily life. It enhances access to public services and both physical and service sector resources (e.g., bridges, buildings, aerospace, pipeline, wind energy generation as well as land and water transport infrastructure). The aging and deterioration of engineered infrastructure across the developed world have therefore become a universal challenge for governments and industries. Thus, monitoring structural integrity to enhance the sustainability and reliability of both new and old structures, and the reduction of their life cycle costs have become increasingly important.Accumulation of damage over the lifespan of a structure without adequate and timely inspection can lead to catastrophic failure. Various damage detection techniques have been developed over many years. These are typified by conventional ultrasonic, acoustic emission, eddy-current, vibration-based and guided wave techniques [1][2][3][4][5][6][7] . Vibration-based and guided wave techniques in particular have attracted much attention. Vibration-based techniques detect damages using the dynamic characteristics of structures have been extensively studied in the last two decades 8-12 but detection of damage mainly depends on successfully recognising changes in the vibration characteristics of the structures being tested. Guided wave techniques, on the other hand, use mechanical stress waves propagated at ultrasonic frequencies along natural boundaries in t...

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“…θ g m based on equation (27). The position at ( 1 m + )-th iteration is updated using equation (28).…”

Section: Hybrid Optimization Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian model updating approach for experimental identification of damage in beams using guided waves

2014

Structural Health Monitoring

View full text Add to dashboard Cite

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“…As one of the greatest challenges of the SHM system has been recognised, the knowledge of what physical parameters observe and how to identify their 'changes' [4]. There have been defined various techniques of efficient damage detection based on modal analysis [5][6][7][8][9][10][11], wave propagation [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], impedance changes [31,32], ultrasonic inspection [33][34][35][36][37][38], and many others [39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Spectral Methods for Modelling of Wave Propagation in Structures in Terms of Damage Detection—A Review

Palacz

2018

Applied Sciences

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Modern methods of detection and identification of structural damage direct the activities of scientific groups towards the improvement of diagnostic methods using for example the phenomenon of mechanical wave propagation. Damage detection methods that use mechanical wave propagation in structural components are extremely effective. Many different numerical approaches are used to model this phenomenon, but, due to their universal nature, spectral methods are the most commonly used, of which there are several types. This paper reviews recent research efforts in the field to show basic differences and effectiveness of the two most common spectral methods used for modelling the wave propagation problem in terms of damage detection.

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“…In this last technique [2][3][4][5][6], acoustic waves are excited along the pipeline and the scattered signals are detected by distributed sensors mounted on the outer wall of the pipelines. Scattering occurs at non-axisymmetric features, which make guided waves suitable for damage detection via signal analysis [7,8]. In addition, attenuation occurs at strong impedance transitions from the pipe wall to the surrounding material, such as external coating, burial, or internal fluid [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Simplified Ultrasonic Damage Detection in Fluid-Filled Pipes

Vogelaar

Golombok

2017

J Nondestruct Eval

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The location and extent of damage in a pipe can be remotely determined from weld and internal damage reflections using a single acoustic emitter/sensor pair. The use of normalised reflections yields single numbers enabling long distance data collection techniques such as wireless hopping. The attenuation is twice as high for opposite inner and outer fluids (whether air and water, or water and air) as compared to identical inner and outer fluids. The absolute recorded signals in the water-filled pipe are attenuated by a factor two compared to the empty pipe. The axial length of detection is reduced by a half. The reduction of >90% in sensors and the longer axial detection (>10× current state-of-theart-technology) means that permanent fixed sensor pairs for whole pipelines are on the horizon of possibility. The greatest advantage is envisioned in submersed pipelines.

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Damage identification by wavelet analysis

Cited by 83 publications

References 14 publications

Bayesian model updating approach for experimental identification of damage in beams using guided waves

Bayesian model updating approach for experimental identification of damage in beams using guided waves

Spectral Methods for Modelling of Wave Propagation in Structures in Terms of Damage Detection—A Review

Simplified Ultrasonic Damage Detection in Fluid-Filled Pipes

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