This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Non-destructive techniques are used widely in the metal industry in order to control the quality of materials. Eddy current testing is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. This paper includes an overview of the fundamentals and main variables of eddy current testing. It also describes the state-of-the-art sensors and modern techniques such as multi-frequency and pulsed systems. Recent advances in complex models towards solving crack-sensor interaction, developments in instrumentation due to advances in electronic devices, and the evolution of data processing suggest that eddy current testing systems will be increasingly used in the future.

/pdf/non-destructive-techniques-based-on-eddy-current-testing-ekomtngkyg.pdf

Non-Destructive Techniques Based on Eddy Current Testing

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The Essential Physics Of Medical Imaging

Magnetism and Magnetic Materials

A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

Pulsed Eddy current (PEC) is a new emerging NDT technique for sub-surface defect detection. The technique mainly uses the response peak value and arrival to detect and quantify the defects. This could suffer from noise and be not sufficient to extract more information about the defects, e.g. depth information of defects. This paper introduces the application of principal component analysis in extracting information from PEC responses. A comparative test carried out shows that the introduced technique has performed better than the conventional technique in the classification of defects.

A feature extraction technique based on principal component analysis for pulsed eddy current ndt

This paper proposes the use of multiple sensors in pulsed eddy-current detection for three-dimensional (3-D) subsurface flaw imaging. A normalization technique has been proposed to eliminate the characteristic variation among the Hall devices used in the probe and lift off effects. A principal component analysis-based feature extraction that provides orthogonal information for multiple sensor fusion has been introduced and investigated. Using the features of multiple projection coefficients, 3-D surface flaws can be measured and reconstructed. The experimental tests have illustrated that the proposed method has delivered more defect information than the conventional peak value and time for pulsed eddy-current sensors.

Multiple sensors on pulsed eddy-current detection for 3-D subsurface crack assessment

The objective of this study is to identify defects such as surface cracks, subsurface defects and metal losses using feature based pulsed eddy current sensors. A new feature, termed as the rising point, related to the propagation time of electromagnetic waves in metallic targets is proposed for defect classification. Experimental studies of the validation, robustness of the new feature of rising time are reported. In addition to other features, defects can be detected and quantified robustly and lift-off can also be derived from the rising time. Conclusion and further work are derived on the basis of the findings.

Defect classification using a new feature for pulsed eddy current sensors

The lift-off effect is commonly known to be one of the main obstacles for effective eddy current NDT testing as it can easily mask defect signals. Pulsed eddy current techniques, which are believed to be potentially rich of information, are also sensitive to the effect. An approach using normalisation and two reference signals to reduce the lift-off problem with pulsed eddy current techniques is proposed. Experimental testing on the proposed technique and results are presented in this report. Results show that significant reduction in the effect has been achieved mainly in metal loss and sub-surface slot inspection. The technique can also be applied for measurement of metal thickness beneath non-conductive coatings, microstructure, strain/stress measurement, where the output is sensitive to the lift-off effect.

Reduction of lift-off effects for pulsed eddy current NDT

Pulsed eddy current (PEC) non-destructive testing and evaluation (NDT&E) has been around for some time and it is still attracting extensive attention from researchers around the globe, which can be witnessed through the reports reviewed in this paper Thanks to its richness of spectral components, various applications of this technique have been proposed and reported in the literature covering both structural integrity inspection and material characterization in various industrial sectors To support its development and for better understanding of the phenomena around the transient induced eddy currents, attempts for its modelling both analytically and numerically have been made by researchers around the world This review is an attempt to capture the state-of-the-art development and applications of PEC, especially in the last 15 years and it is not intended to be exhaustive Future challenges and opportunities for PEC NDT&E are also presented

/pdf/pulsed-eddy-current-non-destructive-testing-and-evaluation-a-1gyp0wcii7.pdf

Ali Sophian

Papers

A feature extraction technique based on principal component analysis for pulsed eddy current ndt

Multiple sensors on pulsed eddy-current detection for 3-D subsurface crack assessment

Defect classification using a new feature for pulsed eddy current sensors

Reduction of lift-off effects for pulsed eddy current NDT

Pulsed Eddy Current Non-destructive Testing and Evaluation: A Review