Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks

Buck, Jeremy A.; Underhill, P. R.; Morelli, Jordan; Krause, Thomas W.

doi:10.1109/tim.2016.2514778

Cited by 54 publications

(28 citation statements)

References 25 publications

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“…The experimental apparatus used here is described in detail in [5] and [10]. Four 25 mm long 410 SS samples, with hole IDs shown in Table 1, were used to simulate different states of corrosion in a uniform ferromagnetic drilled support structure.…”

Section: Methodsmentioning

confidence: 99%

“…The ANN, described in detail in reference [10], consisted of a simple, single hidden-layer architecture along with an input and output layer. MPCA scores from all of the paired pickup coils were used in both models to effectively average out any coil imbalances.…”

Section: Model Descriptionsmentioning

confidence: 99%

“…Again this may be attributed to nonconstant variance, but with an inverse result from that of Hole ID, since signal responses are larger at greater fret depths and the weak signal at zero fret depth is more influenced by tube positioning effects. In the case of ANN, input of fret depth has resulted in even higher accuracy estimates of the remaining parameters [10]. …”

Section: Model Descriptionsmentioning

confidence: 99%

“…Extending this problem to include outer diameter (OD) SG tube fretting creates a four variable problem that may arise under real inspection conditions when overlapping degradation modes are present. This situation has been investigated recently using Artificial Neural Networks (ANN) analysis of MPCA scores [10]. Artificial neural networks (ANNs) are a machine learning method modeled after the neuron interconnections observed in nature [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

Buck

Underhill

Morelli

et al. 2017

AIP Conference Proceedings

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Abstract. Degradation of nuclear steam generator (SG) tubes and support structures can result in a loss of reactor efficiency. Regular in-service inspection, by conventional eddy current testing (ECT), permits detection of cracks, measurement of wall loss, and identification of other SG tube degradation modes. However, ECT is challenged by overlapping degradation modes such as might occur for SG tube fretting accompanied by tube off-set within a corroding ferromagnetic support structure. Pulsed eddy current (PEC) is an emerging technology examined here for inspection of Alloy-800 SG tubes and associated carbon steel drilled support structures. Support structure hole size was varied to simulate uniform corrosion, while SG tube was off-set relative to hole axis. PEC measurements were performed using a single driver with an 8 pick-up coil configuration in the presence of flat-bottom rectangular frets as an overlapping degradation mode. A modified principal component analysis (MPCA) was performed on the time-voltage data in order to reduce data dimensionality. The MPCA scores were then used to train a support vector machine (SVM) that simultaneously targeted four independent parameters associated with; support structure hole size, tube off-centering in two dimensions and fret depth. The support vector machine was trained, tested, and validated on experimental data. Results were compared with a previously developed artificial neural network (ANN) trained on the same data. Estimates of tube position showed comparable results between the two machine learning tools. However, the ANN produced better estimates of hole inner diameter and fret depth. The better results from ANN analysis was attributed to challenges associated with the SVM when non-constant variance is present in the data.

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Section: Methodsmentioning

confidence: 99%

Section: Model Descriptionsmentioning

confidence: 99%

Section: Model Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

Buck

Underhill

Morelli

et al. 2017

AIP Conference Proceedings

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“…In 2016 an ANN with the statistical technique of principal component analysis PCA was applied to steam generator data in simultaneous measurement by using PEC. Combining PCA and ANN improves the sensitivity of pulsed eddy current PEC [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

et al. 2017

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Non-destructive evaluation (NDE) plays an important role in many industrial fields, such as detecting cracking in steam generator tubing in nuclear power plants and aircraft. This paper investigates on the effect of the depth of the defect, width of the defect, and the type of the material on the eddy current signal which is modeled by an adaptive neuro-fuzzy inference system (ANFIS). A total of 60 samples of artificial defects are located 20 mm parallel to the length of the block in each of the three types of material. A weld probe was used to inspect the block. The ANFIS model has three neurons in the input layer and one neuron in the output layer as the eddy current signal. The used design of experiments (DOE) software indicates that the model equations, which contain only linear and two-factor interaction terms, were developed to predict the percentage signal. This signal was validated through the use of the unseen data. The predicted results on the depth and width of defect significantly influenced the percentage of the signal (p < 0.0001) at the 95% confidence level. The ANFIS model proves that the deviation of the eddy current testing measurement was influenced by the width and depth of the defect less than the conductivity of the materials.

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Fast and efficient inversion methods for crack sizing using Bz magnetic signature

Barrarat,

Bensaid,

Rayane

et al. 2023

Int J Numerical Modelling

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Fast estimation is a critical feature of the proposed modeling approach as a “forward” fast solver that has a key role in solving the “inverse” problem involved in crack sizing, especially for real‐time applications. To this end, two different inversion methods have been proposed to estimate the crack depth. The first one uses an interpolation model of the 3D direct model simulation data. The second approach is based on artificial neural networks (ANN). In this article, the rotating uniform eddy current (RUEC) probe is used to detect the normal magnetic component Bz which is defined as the characteristic signal for reconstructing the crack length and depth concurrently. Using the first approach, The Bz characteristic 3D surface is presented, and this can be modeled by a fitted polynomial interpolation equation. Thus, the crack depth can be inverted using this equation referring to the experimental or simulated Bz signal and the crack length. The ANN is outlined to determine the crack depth based on the simulated Bz signature. The move from the first inversion method to the second was flexible and useful, in which the interpolation model is used as a defect signal generator for fast building of a large and efficient database for ANN training. Both of the proposed methods proved the objectivity and accuracy of the inversion results and offered more robust engineering support for automated NDT, reducing production time and increasing productivity.

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Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks

Cited by 54 publications

References 25 publications

Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

Fast and efficient inversion methods for crack sizing using Bz magnetic signature

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