Chapter 2 - Magnetic Sensor Based Pulsed Eddy Current for Defect Detection and Characterization

Pulsed eddy current (PEC) testing technology has been widely used in the field of non-destructive testing of metal grounding structures due to its wide-band excitation and response characteristics. However, multi-source noise in industrial environments can significantly degrade the performance of PEC sensors, thereby limiting their detection accuracy. This study proposes a multi-modal joint pulsed eddy current signal sensor denoising method that integrates the inductive disturbance mechanism. This method constructs the Improved Whale Optimization -Variational Mode Decomposition-Singular Value Decomposition-Wavelet Threshold Denoising (IWOA-VMD-SVD-WTD) fourth-order processing architecture: IWOA adaptively optimizes the VMD essential variables (K, α) and employs the optimized VMD to decompose the perception coefficient (IMF) of the PEC signal. It utilizes the correlation coefficient criterion to filter and identify the primary noise components within the signal, and the SVD-WTD joint denoising model is established to reconstruct each component to remove the noise signal received by the PEC sensor. To ascertain the efficacy of this approach, we compared the IWOA-VMD-SVD-WTD method with other denoising methods under three different noise levels through experiments. The test results show that compared with other VMD-based denoising techniques, the average signal-to-noise ratio (SNR) of the PEC signal received by the receiving coil for 200 noise signals in different noise environments is 24.31 dB, 29.72 dB and 29.64 dB, respectively. The average SNR of the other two denoising techniques in different noise environments is 15.48 dB, 18.87 dB, 18.46 dB and 19.32 dB, 27.13 dB, 26.78 dB, respectively, which is significantly better than other denoising methods. In addition, in practical applications, this method is better than other technologies in denoising PEC signals and successfully achieves noise reduction and signal feature extraction. This study provides a new technical solution for extracting pure and impurity-free PEC signals in complex electromagnetic environments.

We study the interaction of pulsed eddy current (PEC) with metal surface cracks using various coils that have different geometric sizes. In the previous work, we have showed that the PEC technique can be used to inspect electrical-discharge-machined (EDM) notches with depth from 0.5 mm to 9 mm. The results showed that the relationship between PEC signals and crack depth is obvious. In this work, we further try a series of coils with different radii, heights, turns and shapes. We will discuss the effects of these coil parameters on the PEC signal. Some other critical problems of PEC measurements such as signal drift that caused by heating effect of coil currents will be studied. We also show more experiments on fatigue cracks to demonstrate the capability of PEC technique for cracks inspection.

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.

In the current iron and steel industry production, the detection of the yield strength of ferromagnetic materials relies on loss detection. By determining the yield strength of the material through tensile tests, the material needs to be stretched to the yield limit, which greatly increases the cost of testing. This paper presents a method for estimating the yield strength parameters of ferromagnetic materials based on pulsed eddy currents. Firstly, the characteristics of time-domain and frequency-domain of pulsed eddy current response signals are extracted. Then, the back propagation neural network model of each signal characteristic and material yield strength is established, and finally the neural network is used to predict the yield strength of the material. Using the BP neural network model trained by features of pulsed eddy current, the yield strength of the specimens can be estimated. As a non-destructive testing method, the method has a prediction error of 5% or less, and has a certain practical value for reducing the detection cost of industrial production and improving the detection efficiency.

In order to know the effect of surface irregularity in the detection of local wall thinning of pipeline using pulsed eddy current (PEC), the lift-off effects on PEC signal have been investigated. Three kinds of parameters in the PEC signal, which is "peak amplitude", "time to peak amplitude" and "time to zero crossing" are analyzed to separate the lift-off effects in the PEC signal. The distance from sensor to the bottom of sample which is the total thickness of combined insulator and sample is kept constant. The magnitude of the differential peak amplitude is increased with increasing sample thickness, the time to peak amplitude is increased with increasing the sample thickness. To determine the effect of lift-off, a number of balanced transient responses combining wall thinning locations and lift-off distances were plotted.

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.

Pulsed eddy current (PEC) inspection is widely used in nondestructive evaluation of the integrity of conductive structures employed in such engineering fields as aerospace, energy, nuclear, and transportation. In contrast to conventional eddy current (EC) inspection, in PEC magnetic field signals in lieu of impedance signals are obtained during the course of inspection. It is noted that liftoff intersection (LOI) can be found also in PEC with variant liftoffs, whose analytical expressions are beneficial to forward and inverse problems of PEC. In this paper, the generalized closed-form expression of LOI occurring in magnetic field signals, which are acquired from the solid-state magnetic field sensors placed in arbitrary locations within the excitation coil, is formulated based on extended truncated region eigenfunction expansion (ETREE) modeling of PEC. Following this, regarding a coated thick plate, an inverse model based on the analytical expressions of LOI is established in a bid to inversely retrieve the thickness of the nonconductive coating and the conductivity of the base material simultaneously. The inverse model is verified by experiments. It has been found that the estimated values of the two unknown parameters have good agreement with the true values, which indicates that the inverse model is valid and applicable to evaluation of coated materials.

Feature extraction and selection for defect classification of pulsed eddy current NDT

In this study, we introduce a novel auto-compensation method designed to enhance the accuracy of pulsed eddy current (PEC) measurements, crucial for non-destructive testing (NDT) in assessing the integrity of high-voltage feeder pipes. We explicitly target the unique challenges posed by strong electromagnetic interference (EMI) from internal power lines carrying three-phase alternating current. To address them, we propose a compensation algorithm that accounts for spatially varying magnetic permeability in the piping materials. The proposed method integrates multi-physics simulations of the interactions between the power lines, the pipe, and the PEC probe. Two finite element method (FEM) simulation models are developed: the Cable-Pipe Model, simulating the magnetic flux density distribution around the pipe due to internal power lines, and the PEC-Pipe Model, simulating the PEC sensing response considering the circumferentially varying magnetic permeability. To bridge the gap between computational accuracy and field deployment requirements, novel surrogate models are developed based on parametric FEM simulation datasets, allowing rapid approximation of characteristic decay time constants and nominal wall thickness estimations. Field tests validated our method’s effectiveness, demonstrating a measurable reduction in false-positive indications after the physics-based compensation. This work challenges the conventional assumption of constant magnetic permeability in PEC inspections and extends the applicability of PEC for flaw detection in high-voltage feeder pipes and other challenging inspection scenarios, contributing to improved safety and infrastructure longevity. 1

The problem of corrosion in the industrial oil and gas pipes has been one of the major contributors in catastrophic structural failures. Among the various types of corrosion, corrosion under insulation (CUI) has been known to cause serious problems. Pulsed eddy current (PEC) non-destructive testing has shown its effectiveness in detecting hidden CUI. Most PEC systems have been developed by using an inductive coil as their sensing device, while some use a magnetic sensor which potentially offers better resolution. A new probe design based on a solid-state Tunnel magnetoresistance (TMR) is presented. TMR sensors offer higher sensitivities compared to other commercially available sensors. The performance of the probe is evaluated by using ferromagnetic mild steel plates that have thicknesses in the range of 8mm to 12mm at different stand-offs with a thin aluminium sheet under the probe. The different thicknesses represent different corrosion levels, while the stand-offs and thin aluminium sheet are to mimic the insulation of different thicknesses and the cladding in the real pipeline structure. The results show an overall mean absolute error (MAE) of 0.19 mm, which is better than our existing Hall-device-based PEC probe.

An array type probe for monitoring metal tubes is proposed in this paper which utilizes peak value and peak time of a pulsed eddy current(PEC) signal. The probe consists of an array of encircling coils along a tube and the outside of coils is shielded by ferrite to prevent source magnetic fields from directly affecting sensor signals since it is the magnetic fields produced by eddy currents that reflect the condition of metal tubes. The positions of both exciter and sensor coils are consecutively moved automatically so that manual scanning is not necessary. At one position of send-receive coils, peak value and peak time are extracted from a sensor PEC signal and these data are accumulated for all positions to form an array type peak value signal and an array type peak time signal. Numerical simulation was performed using the backward difference method in time and the finite element method for spatial analysis. Simulation results showed that peak value increases and the peak appears earlier as the defect depth or length increases. The proposed array signals are shown to be excellent in reflecting the defect location as well as variations of defect depth and length within the array probe.

Multilayer structures are widely used in aircraft fuselage. Because of the interlayer air gap caused by deformation or disbonding, conventional single-frequency eddy current cannot discriminate between second-layer defect signals and gap signals. In this paper, several defects at varied locations (i.e., first-layer surface, first-layer subsurface, second-layer surface, and second-layer subsurface) are manufactured into two-layer Al-Mn 3003 alloy specimen with various air gaps. Pulsed eddy current (PEC) is investigated in combination with principal component analysis (PCA) to classify and locate defects in the specimen. The new feature named differential frequency to zero is proposed, and the frequency responses of selected frequency band are processed through PCA. The principal components are used for locating defects. The experimental results show that first-layer surface defects, first-layer subsurface defects, second-layer surface defects, and second-layer subsurface defects can be classified when air gap is varied from 0 to 1.4 mm through the proposed methods. In conclusion, PEC testing with the help of PCA can eliminate the interlayer air gap and liftoff effect, which has potential for defect characterization in multilayer aircraft structures.

Corrosion of steel structures, such as lighting and road marker poles, located in close proximity to or buried within the ground have become a social problem. Steel corrosion has been observed to occur rather easily near the ground level. However, corrosion detection in parts of steel structures buried within the ground is very difficult. To address this concern, a magnetic resistance testing system for detecting corrosion in buried steel structures has been developed using a tilted magnetic sensor probe, and it is based on our independently developed extremely low-frequency eddy-current testing methodology. The magnetic sensor probe comprises an anisotropic magnetic resistance sensor, induction coil, and small cancellation coil. The applied magnetic field was operated at extremely low frequencies between 1 and 100 Hz, and the magnetic spectrum, which was traced using the real and imaginary parts of the detected magnetic signal at each frequency, was analyzed for estimating changes in steel thickness. Test samples comprising 4 mm thick stainless steel plates with a partial thinning zone varying in depth between 0.5 and 3.0 mm were used during measurements. Changes in thickness were detected at locations other than the thinning zone by using the differential-magnetic-intensity-subtracted magnetic vector of frequency 1 Hz from that of frequency 20 Hz detected by the tilted magnetic sensor probe. Moreover, the depth of corrosion and its corresponding location beneath the ground level were estimated via measuring techniques involving the use of magnetic sensor probes in the separated position or at different tilt angles in the same position.

Steel microstructure determines the mechanical properties of steel such as hardness and tensile strength. To monitor steel microstructure evolution during thermal processing such as hot rolling, online measurement of microstructure is vital to steel quality control. Previous studies using multi-frequency eddy-current testing to monitor the change of electromagnetic properties and infer steel microstructures have achieved remarkable success, yet due to the high-frequency excitation signal, the inspected thickness of steel microstructures is constrained on the surface due to the skin effect. This study introduces the pulsed eddy-current (PEC) method to characterise the electromagnetic property profile along the depth of thick steel plates. The low-frequency square wave signal enables PEC to reflect multi-layer characteristics in a single measurement with good penetration ability. An explicit analytical equation for calculating the time domain PEC response is derived for the first time which shows advantages in terms of speed and gaining in-depth physical insights. A custom PEC system has been designed and built with a TMR sensor as the receiver. Theoretical and numerical analysis are performed. A time domain intersection point on the Tau curve is found to be a useful indication of the properties of a planar structure. Experimental results on layered planar structures of various electromagnetic properties are analysed and validated.

Due to a harsh working environment, micro-cracks in metal structures (e.g., airplane, oil/gas pipeline, hydro-turbine) often lead to serious accidents, so health monitoring of the metals is of great significance to ensure their safe operation. However, it is hard to perform quantitative detection of multiple micro-cracks by a single nondestructive testing (NDT) technique because of their limits. To monitor for multiple micro-cracks in metals, a Traditional Eddy Current (TEC) and Pulsed Eddy Current (PEC) fusion NDT technique is proposed in this paper. In the proposed technique, the TEC technique is adopted to seek the locations of the micro-cracks in the whole of the metal, while the PEC technique is adopted to acquire information on the depth of micro-cracks automatically according to the location information by the TEC. The experiments indicate that the TEC–PEC fusion NDT system can localize the micro-cracks as well as detect the micro-cracks quantitatively and automatically; therefore, it can be applied in structural health monitoring of metal equipment or in picking candidate components in re-manufacturing engineering.