Pulsed Eddy Current Research Articles

Development of a Cone-Shaped Pulsed Eddy Current Sensor

Compared with the conventional eddy current, pulsed eddy current (PEC) technology has the characteristic of abundant components in the frequency domain, which is a significant technological advancement. Improvements in the production and performance of PEC sensors are necessary for nondestructive testing. Sensor configuration parameters have significant influence on the development of PEC system. In this study, through finite element simulation and experimental investigation, different optimization methods for PEC sensors are compared and studied. According to the eddy current detection principle, a two-dimensional finite element simulation model was established to study the influence of the shape and size parameters of cone-shaped PEC sensors. Then a cone-shaped PEC sensor was optimized by the orthogonal methodology and the response surface methodology, respectively. To compare the effectiveness of the optimization methods, two optimized sensors were fabricated. It was demonstrated that the combination of finite element method and response surface methodology was superior for improving the detection performance of the PEC sensor compared with the orthogonal methodology. The result also indicated that the developed cone-shaped PEC sensor had a stronger defect detection capability than cylindrical-shaped PEC sensors. The proposed optimization design method provides a feasible reference method for the design of PEC sensors and corresponding parameters.

Quantitative analysis of wall thinning of bimetallic clad steel tube based on pulsed eddy current

AbstractBimetallic clad steel tube (BCST) has been widely adopted in onshore and offshore oil and gas transportation due to its excellent corrosion resistance and mechanical properties. Aged BCSTs are susceptible to the initiation of various types of defects, including corrosion, dents, cracking/leakage, and wall‐thinning defects. However, due to the complex structure of BCST and the different materials of inner and outer tubes, it is difficult to implement conventional nondestructive testing. In light of this, a finite element simulation model is established based on the pulsed eddy current testing technology in this paper. Through a series of simulation analyses, the influence of tubing material, defect location, and other factors on the detection results is intensively investigated. By establishing the mapping relationship between the detection signal characteristics and the wall thickness reduction ratio, it is confirmed that there is a quadratic relationship between the differential voltage peak value and the wall thickness reduction, and the defect identification and quantitative evaluation are carried out. The validity of the proposed defect identification and quantitative evaluation method is verified by experiments, which provides a theoretical basis for BCST nondestructive testing and state maintenance. This work facilitates BCSTs structural integrity management and state maintenance decisions, which are critical to the safe operation of the oil and gas system.

A Novel Pulsed Eddy Current Criterion for Non-Ferromagnetic Metal Thickness Quantifications under Large Liftoff.

The pulsed eddy current (PEC) inspection is considered a versatile non-destructive evaluation technique, and it is widely used in metal thickness quantifications for structural health monitoring and target recognition. However, for non-ferromagnetic conductors covered with non-uniform thick insulating layers, there are still deficiencies in the current schemes. The main purpose of this study is to find an effective feature, to measure wall thinning under the large lift-off variations, and further expand application of the PEC technology. Therefore, a novel method named the dynamic apparent time constant (D-ATC) is proposed based on the coil-coupling model. It associates the dynamic behavior of the induced eddy current with the geometric dimensions of the non-ferromagnetic metallic component by the time and amplitude features of the D-ATC curve. Numeral calculations and experiments show that the time signature is immune to large lift-off variations.

An Advanced Magnetoacoustic Compound Inspection Method of High-Order Mode EMAT

In this study, an advanced magnetoacoustic compound inspection method for a high-order Lamb waves mode electromagnetic acoustic transducer (EMAT) was proposed. The proposed EMAT can achieve the excitation and reception of the A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode which is the first-order Lamb waves mode. According to the characteristics of the A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode EMAT testing signal, a signal processing method for obtaining the pulsed eddy current signal by curve fitting was developed to extract the information for the pulsed eddy current. The voltage of the ultrasonic waves was carried on the pulsed eddy current voltage, and it was obtained by removing the pulsed eddy current voltage and extracting the time-domain testing signal of the A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode EMAT. Through A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode EMAT single detection that was used to obtain the pulsed eddy current and A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode, the multi-parameter from the magnetoacoustic compound testing signal of the A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> mode EMAT could be used to characterize the aluminum plate performance degradation. The detection accuracy of aluminum plate performance degradation was improved by the magnetoacoustic compound inspection compared with a single detection method.

Constrained neural classifier training method for flaw detection in industrial pipes using particle swarm optimisation

A novel method for constrained training of multi-class artificial neural network classifiers is proposed in this work. The traditional training procedure is usually based on mean square error minimisation and thus, all classes of interest are considered as having the same relevance for system performance. This is not always the case for real-world applications in which the class relevance may be unbalanced. In this paper, cost functions designed to introduce classification performance constraints for specific classes are presented and particle swarm optimisation is used as global optimisation method. The proposed method is applied to a non-destructive evaluation decision support problem using pulsed eddy currents signals. Experimental results obtained from thermally insulated industrial pipes indicate the efficiency of the proposed method in comparison to neural networks trained from the traditional back-propagation algorithm.

Development of a Wireless Corrosion Detection System for Steel-Framed Structures Using Pulsed Eddy Currents

Structural health monitoring (SHM) can be more efficient with the application of a wireless sensor network (WSN). However, the hardware that makes up this system should have sufficient performance to sample the data collected from the sensor in real-time situations. High-performance hardware can be used for this purpose, but is not suitable in this application because of its relatively high power consumption, high cost, large size, and so on. In this paper, an optimal remote monitoring system platform for SHM is proposed based on pulsed eddy current (PEC) that is utilized for measuring the corrosion of a steel-framed construction. A circuit to delay the PEC response based on the resistance–inductance–capacitance (RLC) combination was designed for data sampling to utilize the conventional hardware of WSN for SHM, and this approach was verified by simulations and experiments. Especially, the importance of configuring sensing modules and the WSN for remote monitoring were studied, and the PEC responses caused by the corrosion of a specimen made with steel were able to be sampled remotely using the proposed system. Therefore, we present a remote SHM system platform for diagnosing the corrosion condition of a building with a steel structure, and proving its viability with experiments.

Determining Feature Extraction Parameters for Pulsed Eddy Current Sensor: A Minimisation Problem Approach

Pulsed eddy current (PEC) is an electromagnetic non-destructive testing (NDT) technique mainly used to inspect corrosion in pipelines. Like many other NDT techniques, to quantify pipe wall thickness, a signal feature has to be extracted from the PEC response. The authors’ previous work has exploited the linear relationship of the time-derivative feature to propose an in-situ calibration routine. However, to extract the time-derivative feature, two configuration parameters need to be determined prior to feature extraction. To the authors’ knowledge, none of the previous works explores the determining factors of this configuration parameters, where the current technique is still limited to using brute force method or trial-and-error. This paper brings novelty by formulating the aforementioned problem as a minimisation problem, and solving it using a particle swarm optimisation (PSO) algorithm. The obtained results, compared against brute force method, demonstrate the feasibility and performance improvement of using PSO in determining the configuration parameters. The approach thus presented is validated and the results obtained are justified by analysing the underlying physical system theory.

Decoupling the Influence of Wall Thinning and Cladding Thickness Variations in Pulsed Eddy Current Using Principal Component Analysis

Corrosion may develop and grow on steel pipes under layers of insulation and cladding. Inspection of the pipes through these protective layers is of paramount importance. Pulsed eddy current (PEC) is a primary non-destructive testing (NDT) technique candidate for this type of inspection as it requires no contact with the inspection material. To overcome the variability in PEC signals due to variations in the cladding thickness, a large measurement set is analysed in this paper using principal component analysis (PCA). The PCA approach decomposes the signal set into a number of uncorrelated variables that explain the maximum amount of the variance in the data set, in which, in this respect, efficiently separate the influences contributed by the difference in the material properties of cladding and pipe wall. The feasibility of using PCA to quantify simulated steel pipe wall independent of confounding cladding thickness variations is investigated. It is found that, with sufficient amount of data, the approach can effectively separate the influences contributed by the wall thickness variations from the cladding thickness variations.

Damage Detection of Cross-Plied CFRP Laminates Based on Rectangular Differential Pulse Eddy Current Sensors

ABSTRACT Carbon fiber reinforced polymer (CFRP) laminates are extensively used in aviation due to their excellent material properties. Damages resulted from manufacturing or usage seriously undermine the safety of aircraft structures. Therefore, the efficient detection of the damage in CFRP laminates is important. In this paper, three kinds of common damages including impact damage, crack damage and delamination damage are detected based on pulsed eddy current (PEC) with rectangular differential sensors. Firstly, to overcome shortcomings of traditional PEC sensors, an optimized rectangular differential sensor is proposed in the study. Then, a normalization method is introduced to process the differential signal obtained with the sensor. Finally, the relationship between the size of the damage and the normalized differential signal is investigated. The investigation results show that the rectangular differential sensor can detect the three types of damages. When a new damage object is detected, the measurement step of a reference signal is omitted. The peak value of the normalized differential signal increases with the increase in damage size. The new PEC method has been proved in the study.

Enhanced Feature Extraction Algorithms Using Oscillatory-Mode Pulsed Eddy Current Techniques for Aircraft Structure Inspection

Abstract A review of the existing literature shows that modern pulsed eddy current (PEC) technique for flaw detection in aircraft structure inspection is typically carried out in aperiodic mode. Аt the same time, the unstable characteristic points of the EC signal usually used as informative parameters can restrict the potential of this excitation mode due to significant measurement errors. This article considers an advanced PEC method of NDT based on the oscillatory mode. To obtain the conditions concerned with different modes of EC probe response oscillations, an equivalent scheme of the “testing object – EC probe” system was developed and analyzed. The frequency and attenuation coefficient of natural oscillations are proposed as the informative parameters of the probe signals. The obtained mathematical model of the probe signals allows for the dependence of proposed signal parameters on the characteristics of the testing object to be evaluated. Herein, we first develop algorithmic software for determining and analyzing the discrete amplitude and phase characteristics of PEC NDT signals based on the simulation results. The errors of the natural frequency oscillations and the attenuation coefficient determination as well as the optimal time for its determination are analyzed in order to minimize the possible errors. Next, the proposed informative parameters are experimentally investigated using a set of specimens. The obtained results confirm the possibility of the proposed methodology to enhance the inspection procedures related to the electrical conductivity and geometric parameters measurements as well as the detected defect sizing.

Intelligent embedded system for decision support in pulsed eddy current corrosion detection using Extreme Learning Machine

In this work an intelligent electronic system was proposed to generate, acquire and process Pulsed Eddy Currents (PEC) signals for corrosion detection in carbon steel pipes thermally insulated with composite coating. The system includes analog (excitation circuit and data acquisition) and digital (signal processing, feature extraction and decision support) sub-systems. The proposed signal processing chain comprises feature extraction using both discrete Fourier and Wavelet transforms, combined with information compaction by Principal Component Analysis (PCA) and decision support through intelligent classification techniques. Two neural network architectures were considered for classification, the traditional Multi-Layer Perceptron (MLP) and also the Extreme Learning Machine (ELM). The results obtained from thermally insulated carbon steel pipes subject to corrosion under isolation indicate the efficiency of the proposed method. Combining the ELM technique and data compaction it was possible to achieve a fast-training portable intelligent system for PEC evaluation and decision support.

Performance degradation detection of 12CrMoV steel by magneto-acoustic compound inspection method

The performance degradation of 12CrMoV steel generally occurs under a long-term working period in a high-temperature environment. In order to improve the reliability of a single nondestructive testing method for the performance degradation analysis of 12CrMoV steel, an advanced magneto–acoustic compound inspection method, in which pulsed eddy current (PEC) and ultrasonic waves were obtained from a single electromagnetic acoustic transducer (EMAT) testing signal, was adopted. The magneto–acoustic compound inspection method was carried out on 12CrMoV steel plates heat-treated at different temperatures and times. New parameters, including the slope and peak value of PEC, and the amplitude and attenuation of ultrasonic waves, were introduced to evaluate the performance degradation of 12CrMoV steel. It was found that ultrasonic waves manifested higher sensitivity than eddy current when the microstructure changed significantly. Therefore, the magneto–acoustic compound inspection method is advantageous for evaluating the performance degradation of 12CrMoV steel.

Simultaneous measurement of thickness and lift-off using the tangential component of magnetic flux density in pulsed eddy current testing

The pulsed eddy current (PEC) technique is commonly used in the petrochemical and power generation industries to measure two parameters: the degree of pipe wall corrosion and the thickness of the insulation shield. These two parameters can be evaluated by examining the thickness of conductive materials and the lift-off distance, respectively. To explore a possible technique for simultaneously measuring the thickness and the lift-off, the present study envisaged the development of a PEC testing method based on detecting the tangential component of the magnetic flux density. The tangential component of the magnetic flux density was excited by two racetrack-type coils injecting currents in opposite directions that were picked up by a magnetic sensor. The slope in logarithmic scale and the time-to-peak of the magnetic signal were verified to characterise the features of the thickness and the lift-off, respectively. By analysing the simulation and experimental results, the feasibility of simultaneously measuring the thickness and the lift-off was demonstrated.

Lift-off invariant inductance of steels in multi-frequency eddy-current testing

Eddy current testing can be used to interrogate steels but it is hampered by the lift-off distance of the sensor. Previously, the lift-off point of intersection (LOI) feature has been found for the pulsed eddy current (PEC) testing. In this paper, a lift-off invariant inductance (LII) feature is proposed for the multi-frequency eddy current (MEC) testing, which merely targets the ferromagnetic steels. That is, at a certain working frequency, the measured inductance signal is found nearly immune to the lift-off distance of the sensor. Such working frequency and inductance are termed as the lift-off invariant frequency (LIF) and LII. Through simulations and experimental measurements of different steels under the multi-frequency manner, the LII has been verified to be merely related to the sensor parameters and independent of different steels. By referring to the LIF of the test piece and using an iterative inverse solver, one of the steel properties (either the electrical conductivity or magnetic permeability) can be reconstructed with a high accuracy.

Pulsed eddy current array design and electromagnetic imaging for defects detection in metallic materials

ABSTRACT Defects caused by a harsh working environment and cyclic loading may seriously lead to catastrophic industrial accidents. Therefore, an efficient and accurate detection method is needed to quantitatively characterise the multiple defects of large-area metal components. In this paper, a pulsed eddy current (PEC) array is designed by considering mutual electromagnetic influence between the PEC array; the differential peak values at different scan positions are abstracted as feature signals for defect characterisation, and the relationship between defect width and detection signal is discovered; the detection capability of sensor array for adjacent defects is analysed. Subsequently, the electromagnetic imaging is applied to visualise and quantitatively evaluate the multiple defects, and then, the quantitative detection approaches for the localisation and width of multiple defects in metal components are proposed. An experimental setup is built and the proposed approach is validated by experiment. The results show that the proposed method can effectively determine the locations and widths of defects in metal components within the absolute error of 1 scan step.

Parameter Analysis of Pulsed Eddy Current Sensor Using Principal Component Analysis

Pulsed eddy current (PEC) technique provides a means to inspect structures without surface contact. It is particularly useful when the structure's surface is rough or inaccessible, such as insulated pipes in pipeline. Probe parameters of a PEC system, especially the sensing and excitation coil diameters, can significantly affect the PEC system's performance. Thus, detailed analysis of these parameters is paramount in developing a PEC system. Currently, this is accomplished by establishing the trend of features with respect to the analyzed variables, e.g. sample thicknesses. However, prior to extracting these features, a number of configuration parameters have to be determined. For this reason, analyzing PEC performance over a range of coil diameter values is rather time-consuming as both the sensing and excitation coil diameters significantly affect the received signals. Principal component analysis (PCA) is proposed as an alternative to the feature extraction. The work here analyzes the trends contributed by the PCA scores for different values of sensing and excitation coil parameters. Results from both numerical simulations and experiments suggest that the sensitivity of the PEC probe is highly correlated with the excitation coil diameter, while the excitation-sensing coil distance is not significant in determining the sensitivity of the PEC probe. These findings are consistent with those reported in the literature, suggesting the potential of adopting PCA for an automated PEC performance analysis process.

Time-to-peak of time derivative signals of pulsed eddy current testing for evaluating the thickness of ferromagnetic samples

The time-to-peak serves as a popular signal feature of pulsed eddy current (PEC) signals and is widely used in thickness measurement and defect detection. In order to further improve the ability of time-to-peak independent of the lift-off effect, a time derivative processing method is proposed in this paper to obtain a better time-to-peak feature of time derivative signals of PEC for reducing the lift-off effect. The method is used to improve the thickness measurement accuracy of ferromagnetic samples. The results of simulation and experimentation demonstrate that a time-to-peak feature of time derivative signals of PEC can be obtained using the time derivative processing method and the timeto-peak obtained from time derivative signals of PEC can be used to measure the thickness and improve the thickness measurement accuracy of ferromagnetic samples. This means that the use of the time-to-peak of time derivative signals of PEC is feasible for high-precision evaluation of the thickness of ferromagnetic samples.

Методика на основе импульсных вихревых токов для определения механических свойств ферромагнитных материалов

In the current steel industry production, the detection of the mechanical properties of ferromagnetic materials relies on destructive testing, which greatly increases the cost of production. In this paper, a method is proposed to estimate the mechanical properties of ferromagnetic materials based on the pulsed eddy current (PEC) techniques. Firstly, the traditional features of the PEC signal, such as differential signal peaks and spectral amplitudes, are applied to the quantitative estimation of mechanical properties. Secondly, a method based on the empirical mode decomposition and Hilbert-Huang transform is used to extract the marginal spectral peak and marginal spectral energy as new features. Finally, BP neural network algorithm is introduced to quantitatively estimate the mechanical property parameters. The results show that the combination of both traditional and new features is suitable for mechanical properties estimation. The method has high accuracy for the quantitative estimation of mechanical performance parameters.

Research on pulsed eddy current automatic detection technology for weld defects of pressure vessel cylinder

In order to solve the problems of low detection efficiency, high labor intensity and poor working environment of large pressure vessel cylinder, the pulsed eddy current (PEC) nondestructive testing technology is used to detect the defects in the welding seam of the cylinder. The PEC sensor composed of rectangular probe coil and Hall sensor is installed on the mobile inspection platform to test two simulated cylinder weld samples. Results show that the signal curve of sample 1 without defect is gentle and without mutation, while the signal of sample 2 with defect has three mutation signals. Then, the X-ray testing method is used to detect the sample, radiograph confirmed that there are no defects on sample 1 and three porosity defects in sample 2. Through the comparison and analysis of the above two kinds of detection results, the feasibility of the detection method for weld subsurface defects is verified.

Estimation method of yield strength of ferromagnetic materials based on pulsed eddy current testing

In the current production of iron and steel industry, the testing of mechanical properties of ferromagnetic materials relies on tensile testing, which is time-consuming and destructive, thus greatly increasing the production cost. In order to solve this problem, a method based on pulsed eddy current is proposed to estimate the yield strength of ferromagnetic materials. Eddy current loss and hysteresis loss are the sources of loss in the magnetization process of ferromagnetic materials. Not only the loss but also the yield strength of the material is related to the microstructure of the magnetized material. In this paper, the relationship between loss in the process of magnetization and microstructure of materials is analyzed. The features of eddy current loss and hysteresis loss were found from the pulsed eddy current signals, and the yield strength evaluation model was established by feature fitting. The experimental results show that the model has high evaluation accuracy.