Structural Reliability Prediction Using Acoustic Emission-Based Modeling of Fatigue Crack Growth

Keshtgar, Azadeh; Sauerbrunn, Christine M.; Modarres, Mohammad

doi:10.3390/app8081225

Cited by 21 publications

(18 citation statements)

References 25 publications

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“…Another improvement in AE data analysis was given by Keshtgar et al [18] through a statistical concept, Bayesian analysis. They considered AE data during fatigue monitoring of crack growth.…”

Section: Acoustic Emission Applicationsmentioning

confidence: 99%

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Ono

2019

Applied Sciences

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Section: Acoustic Emission Applicationsmentioning

confidence: 99%

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Ono

2019

Applied Sciences

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“…Also, C and m are model parameters to be determined. Our choice of PoF model is based on existing literature on promising results from AE-based crack growth rate estimation using a Paris-Erdogan model 20,24,40 RUL…”

Section: Proposed Approachmentioning

confidence: 99%

“…Nonetheless, only a few studies in the literature have investigated AE-based damage size and RUL estimation of structures. 20 AE-based analyses using HMMs have been reported 21,22 for estimating the critical damage size and RUL for composite materials when subjected to fatigue loading. On the other hand a hybrid of Bayesian analysis and Paris-Erdogan relation for crack growth rate has been used 23,24 for AE-based estimation of crack size.…”

Section: Introductionmentioning

confidence: 99%

Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion

Aria

Droguett

Azarm

et al. 2019

Structural Health Monitoring

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In this article, a new deep learning-based approach for online estimation of damage size and remaining useful life of structures is presented. The proposed approach consists of three modules. In the first module, a long short-term memory regression model is used to construct a sensor-based estimation of the damage size where different ranges of temporal correlations are considered for their effects on the accuracy of the damage size estimations. In the second module, a convolutional neural network semantic image segmentation approach is used to construct automated damage size estimations in which a pixel-wise classification is carried out on images of the damaged areas. Using physics-of-failure relations, frequency mismatches associated with sensor- and image-based size estimations are resolved. Finally, in the third module, damage size estimations obtained by the first two modules are fused together for an online remaining useful life estimation of the structure. Performance of the proposed approach is evaluated using sensor and image data obtained from a set of fatigue crack experiments performed on aluminum alloy 7075-T6 specimens. It is shown that using acoustic emission signals obtained from sensors and microscopic images in these experiments, the damage size estimations obtained from the proposed data fusion approach have higher accuracy than the sensor-based and higher frequency than the image-based estimations. Moreover, the accuracy of the data fusion estimations is found to be more than that of image-based estimations for the experiment with the largest sensor dataset. Based on the results obtained, it is concluded that the consideration of longer temporal correlations can lead to improvements in the accuracy of crack size estimations and, thus, a better remaining useful life estimation for structures.

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“…However, in many high-tech fields, such as aerospace, only a limited number of failure data samples are available due to the increased component or system reliability, cost constraints, or other reasons. Limited samples lead to the presence of epistemic uncertainty over the distributional parameters, which make the traditional research method based on a large number of samples no longer applicable [5][6][7]. Hence, it is of great significance to develop an effective and efficient reliability analysis method for complex systems under limited samples [8,9].…”

Section: Introductionmentioning

confidence: 99%

System Reliability Assessment with Imprecise Probabilities

Yang

Huang

et al. 2019

Applied Sciences

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The exact statistical characteristics of some components may be unavailable because of the limited sample information in practical engineering. One challenge that system reliability analysis faces is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in the analysis results. In this paper, we propose a procedure for the reliability analysis of complex systems with a limited number of samples. Bayesian inference is used to estimate the parameter intervals of the life distributions of the components with a limited number of samples. Then, probability boxes (p-box) are constructed from the parameter intervals to represent the life distributions of the components with a limited number of samples. In addition, the theory of survival signature is applied to calculate the reliability of the system with a mixture of precise and imprecise knowledge of the life distributions of the components. Finally, two numerical examples are given to illustrate the validity of the methods.

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Structural Reliability Prediction Using Acoustic Emission-Based Modeling of Fatigue Crack Growth

Cited by 21 publications

References 25 publications

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion

System Reliability Assessment with Imprecise Probabilities

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