Probabilistic inference of fatigue damage propagation with limited and partial information

Huang, Min; He, Jingjing; Guan, Xuefei

doi:10.1016/j.cja.2015.06.017

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…[47][48][49][50] Such a variability in fatigue crack initiation life has been demonstrated in many reported fatigue testing datasets, including, but are not limited to the existing literature. [51][52][53][54][55] The time window is calculated as mentioned in section ''Lamb waves propagation theory.'' For demonstration purpose, the signal of F1 in the selected time window is shown in Figure 9.…”

Section: Resultsmentioning

confidence: 99%

Fatigue damage detection from imbalanced inspection data of Lamb wave

Fang

Liu

et al. 2021

Structural Health Monitoring

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The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.

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Section: Resultsmentioning

confidence: 99%

Fatigue damage detection from imbalanced inspection data of Lamb wave

Fang

Liu

et al. 2021

Structural Health Monitoring

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“…Despite a number of studies on Lamb wave damage detection have been reported in existing studies, there are still some challenges for engineering applications [ 16 , 17 ]. First, a great number of experimental datasets of Lamb wave testing are required to establish a reliable and accurate crack size quantification model.…”

Section: Introductionmentioning

confidence: 99%

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data

Ran

Liu

et al. 2017

Sensors

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This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions.

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“…The prognosis of fatigue crack growth is a crucial problem for developing the PHM technology. Traditional crack growth models are accurate if model parameters have been accurately defined [3]. However, fatigue crack growth is a stochastic process affected by numerous uncertainties, which arise from sources such as intrinsic material properties, loading, and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

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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Probabilistic inference of fatigue damage propagation with limited and partial information

Cited by 11 publications

References 23 publications

Fatigue damage detection from imbalanced inspection data of Lamb wave

Fatigue damage detection from imbalanced inspection data of Lamb wave

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data

Application of deterministic resampling particle filter to fatigue prognosis

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