Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method

Chen, Jian; Yuan, Shenfang; Cai, Jing

doi:10.3390/s16030320

Cited by 37 publications

(29 citation statements)

References 35 publications

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“…Yu et al [ 17 ] employed a sparsely arranged piezoelectric sensor array and developed a focusing array algorithm to quantitatively detect the crack using Lamb wave. Chen et al [ 18 ] proposed a Lamb wave-particle filter-based method for on-line crack propagation prognosis. The particle filter provides the probabilistic results for the crack propagation prognosis.…”

Section: Introductionmentioning

confidence: 99%

Lamb Wave Damage Quantification Using GA-Based LS-SVM

Sun

Wang

et al. 2017

Materials

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Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

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Section: Introductionmentioning

confidence: 99%

Lamb Wave Damage Quantification Using GA-Based LS-SVM

Sun

Wang

et al. 2017

Materials

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“…On the other hand, the particle filter (PF) is capable of handling nonlinear and/or non-Gaussian systems without restrictive assumptions, which core idea is to approximate the posterior probability density function (PDF) of the system state by a set of random samples called as particles with associated weights. Recently, the PF is more and more applied for predicting problems [6][7][8][9], especially for the fatigue crack growth prognosis [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…This problem leads to a large error in the prognosis of fatigue crack growth, especially when a small number of particles is required for on-line application. Cadini et al [11], Zio et al [12], Neerukatti et al [13] and Chen et al [14] developed the fatigue crack growth prognosis method based on the basic PF method, but did not discuss the particle impoverishment problem.…”

Section: Introductionmentioning

confidence: 99%

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Application of deterministic resampling particle filter to fatigue prognosis

Yuan¹,

Chen²

2017

J. vibroeng.

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The method based on a particle filter for a fatigue crack growth prognosis has proved to be a powerful and effective tool for developing prognostics and health management (PHM) technology. However, the widely used basic particle filter have the unavoidable particle impoverishment problem, which will make particles unable to approximate the true posterior probability density function of the system state and lead to a prognosis result with a large error. This paper proposes a fatigue crack growth prognosis method based on a deterministic resampling particle filter. The active structural health monitoring based on the Lamb wave is used for on-line crack length monitoring with piezoelectric transducers. With the on-line crack measurement, the crack state and crack growth model parameters are estimated for a fatigue crack growth prognosis. In addition, the deterministic resampling procedure is employed to overcome the particle impoverishment problem. The result shows the proposed crack growth prognosis method based on deterministic resampling particle filter can provide more satisfactory results than the basic particle filter.

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“…Various damage indices have been developed to interpret Lamb wave signals and alleviate noises by transforming Lamb wave signals to different physical domains. The ways to transform Lamb wave signals include short-time Fourier [13], wavelet [14], and Hilbert-Huang [15] transformations. By plotting damage indices at each location, we can produce a damage map to characterize the degree of damage over structure.…”

Section: Introductionmentioning

confidence: 99%

Nonpenetrating Damage Identification Using Hybrid Lamb Wave Modes from Hilbert-Huang Spectrum in Thin-Walled Structures

Wang

Qiao

Shi

2017

Shock and Vibration

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Lamb waves have shown promising advantages for damage identification in thin-walled structures. Multiple modes of Lamb wave provide diverse sensitivities to different types of damage. To sufficiently utilize damage-related wave features, damage indices were developed by using hybrid Lamb wave modes from Hilbert-Huang spectra. Damage indices were defined as surface integrals of Hilbert-Huang spectra on featured regions determined by time and frequency windowing. The time windowing was performed according to individual propagation velocity of different Lamb wave mode, while the frequency windowing was performed according to the frequency of excitation. By summing damage indices for all transmitter-receiver pairs, pixels were calculated to reconstruct a damage map to characterize the degree of damage at each location on structure. Both numerical and experimental validations were conducted to identify a nonpenetrating damage. The results demonstrated that the proposed damage indices using hybrid Lamb wave modes are more sensitive and robust than the one using single Lamb wave mode.

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Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method

Cited by 37 publications

References 35 publications

Lamb Wave Damage Quantification Using GA-Based LS-SVM

Lamb Wave Damage Quantification Using GA-Based LS-SVM

Application of deterministic resampling particle filter to fatigue prognosis

Nonpenetrating Damage Identification Using Hybrid Lamb Wave Modes from Hilbert-Huang Spectrum in Thin-Walled Structures

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