Uncertainty Propagation in Eddy Current Nde Inverse Problems

Aldrin, John C.; Knopp, Jeremy S.; Blodgett, Mark P.; Sabbagh, Harold A.

doi:10.1063/1.3591909

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The variability mentioned above is considered the input uncertainty, which will be integrated into the subsequent inversion stage. In this stage, modeling and analysis are used to derive the predicted parameters that describe the system based on observed measurements or simulated output from the forward procedure [43]. During the inversion process, epistemic uncertainty is introduced, which is related to the learning model parameters and the model itself.…”

Section: Uncertainty Sources Of Mflmentioning

confidence: 99%

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Li,

Deng

2024

Inverse Problems

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Magnetic flux leakage (MFL), a widely used Nondestructive Evaluation (NDE) method, for inspecting pipelines to prevent potential long-term failures. During field testing, uncertainties can affect the accuracy of the inspection and the decision-making process regarding damage conditions. Therefore, it is essential to identify and quantify these uncertainties to ensure the reliability of the inspection. This study focuses on the uncertainties that arise during the inverse NDE process due to the dynamic magnetization process, which is affected by the relative motion of the MFL sensor and the material being tested. Specifically, the study investigates the uncertainties caused by sensing liftoff, which can affect the output signal of the sensing system. Due to the complexity of describing the forward uncertainty propagation process, this study compared two typical machine learning-based approximate Bayesian inference methods, Convolutional Neural Network (CNN) and Deep Ensemble (DE), to address the input uncertainty from the MFL response data. Besides, an Autoencoder method is applied to tackle the lack of experimental data for the training model by augmenting the dataset, which is constructed with the pre-trained model based on transfer learning. Prior knowledge learned from large simulated MFL signals can fine-tune the Autoencoder model which enhances the subsequent learning process on experimental MFL data with faster generalization. The augmented data from the fine-tuned Autoencoder is further applied for machine learning-based defect size classification. This study conducted prediction accuracy and uncertainty analysis with calibration, which can evaluate the prediction performance and reveal the relation between the liftoff uncertainty and prediction accuracy. Further, to strengthen the trustworthiness of the prediction results, the decision-making process guided by uncertainty is applied to provide valuable insights into the reliability of the final prediction results. Overall, the proposed framework for uncertainty quantification offers valuable insights into the assessment of reliability in MFL-based decision-making and inverse problems.

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Section: Uncertainty Sources Of Mflmentioning

confidence: 99%

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Li,

Deng

2024

Inverse Problems

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“…Monte Carlo methods have been previously applied to MAPOD evaluation [7]. New efficient methods such as polynomial chaos theory and probabilistic collocation methods (PCM) will enable the greater application of stochastic models [8,9]. Fundamentally, this evaluation of uncertainty bounds becomes a two-level analysis.…”

Section: Mitigating Samples and Testing Through Model-assisted Evaluamentioning

confidence: 99%

Case Studies for Model-Assisted Probabilistic Reliability Assessment for Structural Health Monitoring Systems

Aldrin¹,

Medina²,

Lindgren³

et al. 2011

AIP Conference Proceedings

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This paper describes progress on the development of a protocol and presents several case studies for model-assisted probabilistic reliability assessment of structural health monitoring (SHM) technologies. The design and progress of an experimental demonstration study is presented highlighting the protocol and test plan. A simulation-based study for an SHM system incorporating vibration methods is also introduced to demonstrate the protocol process. Probability of detection and probability of correct characterization curves were generated for different transducer locations providing key insight on sensor placement and expected detection and characterization performance.

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Uncertainty Quantification and Sensitivity Analysis in Subsurface Defect Detection with Sparse Models

Zygiridis,

Kyrgiazoglou,

Amanatiadis

et al. 2024

J Nondestruct Eval

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Uncertainty Propagation in Eddy Current Nde Inverse Problems

Cited by 5 publications

References 11 publications

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Case Studies for Model-Assisted Probabilistic Reliability Assessment for Structural Health Monitoring Systems

Uncertainty Quantification and Sensitivity Analysis in Subsurface Defect Detection with Sparse Models

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