Alternating Current Field Measurement Research Articles

Bicharacteristic probability of detection of crack under multi-factor influences using alternating current field measurement technique

Alternating Current Field Measurement (ACFM) technique has been widely used in various fields such as oil & gas, aerospace areas. However, during the detection process, the probability of detection (POD) is influenced by multi-factors such as lift-off height, scanning angle and detection speed. In this paper, a bicharacteristic probability of detection (BPOD) model is developed for quantifying the crack detection reliability using integrated Bx and Bz signals in the ACFM technique. A multidimensional BPOD model is established to study the influence of multiple factors on the detection probability of cracks using ACFM technique. A Bayesian network-based method is proposed to reverse the hierarchical impact weights of influence factors on the BPOD of the ACFM technique. The results show that the BPOD model considering both Bx and Bz response signals can evaluate smaller defects than the conventional POD model. The multiple influencing factors can substantially decrease the BPOD of cracks. The hierarchical impact weights on BPOD for cracks are ranked as follows: lift-off height, detection speed, personnel involved in the detection process and scanning angle. This approach can retrospectively find the potential contributors to the missed detections based on the BPOD.

3D visual reconstruction of corrosion in underwater structure using alternating current field measurement technique

Corrosion is a common damage form in various underwater structures. However, the corrosions are usually covered by the coatings and attachments in the underwater environment. In this paper, the 3D visual reconstruction methods for evaluating the corrosion defect using the alternating current field measurement (ACFM) technique are presented. The FEM model is set up with corrosion and pitting defects to analyze the disturbed current field and the distorted magnetic field. A gradient imaging algorithm based on the Bz signal is proposed to reconstruct the surface morphology of the corrosion and the pitting defect. The inner contour of the corrosion defect is reconstructed using the Bx signal sensitive characteristics of probe lift-off. The calibration method is proposed to measure the depth of the pitting defect. The testing system is built, and the experiments are carried out to verify the 3D visual reconstruction methods. The maximum error of the surface area reconstruction is 8.13%. The maximum error of the corrosion inner reconstruction is 10%. The maximum error of the pitting depth is 3.25%.

A novel transition frequency for identification and evaluation of subsurface cracks based on alternating current field measurement technique

Subsurface cracks are more challenging to detect and evaluate compared to surface cracks. This paper introduces a novel transition frequency to identify and evaluate subsurface cracks using alternating current field measurement (ACFM) technique. A theoretical model is proposed to explore current disturbance mechanism for subsurface cracks under different frequencies. A 3D simulation model is established to validate the proposed theoretical model and investigate the effects of crack length, shape and specimen thickness on transition frequency. The ACFM testing system is used to detect subsurface cracks with different burial depths and explore the effect of lift-off on transition frequency. The results demonstrate that, with increasing frequency, the shape of Bx signal for subsurface cracks transitions from a trough to a peak, which can be utilized for identifying subsurface cracks. The burial depth can be accurately assessed by transition frequencies. The transition frequency is hardly affected by crack length, shape and lift-off.

High-speed rail defect detection using multi-frequency exciting 3D ACFM

High load wheel-rail friction leads to surface defects on the rail, which requires an accuracy non-destructive testing (NDT) technology to prevent rail fracture and even major safety accidents. Alternating current field measurement (ACFM) has attracted attention due to its high detection precision and low sensitivity of lift-off and velocity effects. The paper proposes a multi-frequency, three-dimensional (3D) ACFM detection system that can obtain abundant detection information for multi-frequency comprehensive evaluation to improve detection accuracy. The system mainly includes an expandable phase-locked amplifier module and an adjustable multi-frequency signal source module, which provides a hardware IQ demodulation acceleration platform for multi-frequency and multi-channel ACFM detection to obtain enough defect signals for analysis. Analysis of the speed effect indicates that increasing the excitation frequency can improve the stability of the detection signal. We design a quantitative evaluation model for defects with a depth of 2–8 mm and surface angle between 30° and 75°.

Novel semi-ring type alternating current field measurement probe for inspection of coiled tubing

Coiled tubing is a critical equipment in the oil and gas exploration and development field. Due to the high pressure, corrosive medium and repeated buckling, cracks are easily introduced in the surface of the coiled tubing. It is necessary to detect cracks in-service for the maintenance of the coiled tubing. In this paper, a new semi-ring type alternating current field measurement (ACFM) probe is presented for imaging and evaluating the surface cracks in the coiled tubing. The finite element method (FEM) model is developed to analyze the characteristic signals of the crack. According to the simulate results, the tunnel magneto resistance (TMR) sensor arrays in semi-ring ACFM probe is placed between the coiled tubing wall and the coil. The experimental results show that the novel ACFM probe can achieve imaging and detection of surface axial and circumferential cracks in the coiled tubing. The through-wall crack can be judged effectively compared to the shallow crack. The length of axial cracks can be accurately evaluated by the peak-valley spacing of the Bz signal. The length of circumferential crack can be evaluated approximately by the location of sensor arrays in the circumferential direction.

An End-to-End Physics-Informed Neural Network for Defect Identification and 3-D Reconstruction Using Rotating Alternating Current Field Measurement

Alternating current field measurement (ACFM) technique has been widely used in defect detection of metal structures. However, the identification and reconstruction of defects depend on human experience or simple empirical formulas, which leads to misjudgment of defects and large quantization errors. This paper proposes an end-to-end physics-informed neural network for defect identification and three-dimensional (3-D) reconstruction. The high precision automatic detection system with a specially designed probe is established to detect defects in any direction. The Faster-RCNN network is used to identify and classify defects. The physics-informed Pix2Pix network is constructed to realize 3-D reconstruction of defects with different types. The results show that the established end-to-end physics-informed neural network can realize the identification and 3-D reconstruction of defects, in which the mean average precision is 0.9982, the average length error of cracks is 0.9249 mm, the average depth error of cracks is 0.3402 mm, the average volume error of corrosion is 0.0667, and the average maximum depth error of corrosion is 0.3464 mm.

Characterization of intersecting and bifurcating rolling contact fatigue (RCF) cracks in railway rails using ACFM sensor

Rolling contact fatigue (RCF) cracks are threatening to the security of railway rails. Alternating current field measurement (ACFM) is an electromagnetic non-destructive method for RCF cracks detecting and the sizing algorithm has been developed for single and uniform arranged RCF cracks. However, non-uniform cracks with different propagation angles, e.g. intersecting and bifurcating cracks, are difficult to be identified and characterized using the algorithm. In this paper, modelling of ACFM responses on non-uniform RCF cracks are discussed. A novel ACFM By signal and the method for identifying intersecting and bifurcating cracks are proposed. Results show that By signal is more beneficial to detect complex arranged RCF cracks and is more sensitive to variations of intersecting/bifurcating angles and positions than conventional Bx and Bz signals. By signal is promising for sizing intersecting/bifurcating RCF cracks and responses of By signals can contribute to the establishment of sizing algorithm for complex arranged RCF cracks.

Lift-Off Point of Intersection for Eliminating Lift-Off Noise in Alternating Current Field Measurement

Lift-off is a challenging issue in alternating current field measurement (ACFM) technique. The spurious signals caused by lift-off variations are similar to the response signals of defects, resulting in defect misjudgment. In this paper, a lift-off point of intersection (LOI) feature is discovered for the first time from the raw signals of ACFM for the elimination of spurious signals caused by lift-off variations. A theoretical model is established to analyze the effect of lift-off variations on characteristic signals of ACFM. A 3D finite element model is built to analyze the effect of the excitation frequency on LOI. The probe and testing system are established, and the validation experiments are carried out. The results show that the traditional Bx signal is susceptible to the variation of lift-off, and the higher the excitation frequency, the closer the LOI is to a point. The novel Bx signal plotted by LOI amplitude has no spurious signals caused by lift-off variations and can also accurately evaluate the crack depth as with traditional Bx signals.

Welding defect detection in nuclear power plant spent fuel pool panels based on alternating current field measurement: experimental and finite element analysis

The overlay panels of spent fuel pools of nuclear power plants can easily become corroded and produce micro-crack defects. Surface crack defects tend to expand vertically, horizontally and obliquely, causing damage and fracture to the overlay panels and welds of spent fuel pools. Traditional non-destructive testing (NDT) cannot complete underwater testing in real time. In order to improve the timeliness of crack detection and shorten the inspection period, research on accurate inspection technology for surface cracks in the overlay panels of spent fuel pools is carried out in this paper based on alternating current field measurement (ACFM) and the weld defect detection process for the cladding panels of spent pools is optimised. In this work, different types of artificial defect are assumed and the distortion of the magnetic field characteristic signal caused by the defects is studied. The characteristics of magnetic field signals generated in different defect regions are studied by establishing a defect electromagnetic detection model for numerical calculation. Finally, experimental and numerical results are compared and analysed. The results show that ACFM can be used to quickly and effectively inspect for cracks in the base material, weld and interface of spent fuel pool overlay panels and it has the characteristics of accuracy, high resolution, high sensitivity and low delay. The research results, which have good application value, provide technical support for electromagnetic inspection of latent cracks in field spent fuel pools and early crack warning of underwater structural defects.

A Surface Crack Assessment Method Unaffected by Lift-Off Based on ACFM

Alternating current field measurement (ACFM) has been widely used to detect and evaluate metal surface cracks. However, the lift-off caused by various attachments significantly affects the accuracy of crack size assessment. In this article, a simple method is proposed to evaluate surface cracks without the limitation of lift-off. The theoretical models of ACFM and the lift-off effect are developed. The influence of lift-off on magnetic field response signals is studied with the aid of the finite-element method (FEM). The ACFM test system is set up, and the experiments are carried out to evaluate surface cracks of different dimensions at different lift-off distances. The test results show that the length and depth of surface cracks can be accurately evaluated at an unknown lift-off distance by the proposed method.

Novel phase reversal feature for inspection of cracks using multi-frequency alternating current field measurement technique

Aluminum and its alloys have been widely used in aerospace and other industrial fields. Aluminum and its alloy structures are prone to surface and subsurface cracks when they are used in harsh environments. In this paper, a novel phase reversal feature is found to classify and evaluate cracks using the multi-frequency alternating current field measurement (ACFM) technique. The theoretical model of the phase reversal feature is developed. The distorted electromagnetic fields and response signals of surface and subsurface cracks are analyzed by the finite element method. The multi-frequency ACFM testing system is set up. The experiments are carried out to test surface and subsurface cracks. The results show that the response signals of the surface and subsurface cracks have distinct characteristic due to the phase reversal feature using the multi-frequency ACFM technique. The surface and subsurface cracks can be classified by the amplitude reversal phenomenon of the Bz signal caused by the novel phase reversal feature. The buried depth of the subsurface crack can be evaluated accurately by the reversal frequency component.

Rapid Surface Inspection of Steel Bridges Using ACFM

Many bridges feature bolted or welded steel sections as part of their structure. These are susceptible to fatigue cracking due to the cyclic action of traffic and wind forces on the bridge. Since the structure is often painted to reduce corrosion, detection of surface cracks at early stages by conventional means can be slow and costly as the paint must be removed and then reapplied in order to conduct magnetic particle testing (MT). Alternating Current Field Measurement (ACFM) is an electromagnetic inspection technique capable of reliably detecting and sizing surface-breaking fatigue cracks in metals. Importantly, it can do so without the need to remove most paints and coatings. Its common use for the inspection of offshore structures, above and below sea level, have demonstrated the technique’s suitability to inspect large structural welds in difficult conditions, both environmentally and with less than ideal surface conditions. ACFM provides information on the length and depth of detected cracks, allowing repair efforts to be concentrated on the most serious defects, without wasting time on insignificant ones. Recent improvements to ACFM equipment portability, speed of acquisition and sensitivity, combined with the introduction of advanced multi-element array probes, have improved the speed and ease of inspections. Use of the ACFM technique can therefore greatly speed up inspection, reduce waste and save time compared to MT inspection. Civil structures are sometimes corrosion protected using conductive coatings which are known to pose a challenge to electromagnetic inspection techniques. In this article, simulations, experimental results and recommendations related to the application of ACFM on galvanized and thermal sprayed aluminum structures are presented. Examples of various bridges inspections are presented; from a historic span over the river Thames, originally built in 1867, to more recent road and rail crossings.

Visual and Intelligent Identification Methods for Defects in Underwater Structure Using Alternating Current Field Measurement Technique

As the ocean engineering structure serves in the critical underwater environment, a variety of defects, such as cracks and corrosions, always cause damage to the structure. It is still a key challenge to identify and evaluate these defects accurately in the underwater environment. In this article, the visual and intelligent identification methods are presented for the inspection of defects in underwater structures using the alternating current field measurement (ACFM) technique. The current perturbation theory is developed to analyze the disturbed current field and the distorted magnetic field caused by defects. The gradient imaging algorithm is presented as an image preprocessing method to highlight the visual morphology of defects. The underwater intelligent ACFM system is set up. The experiments are carried out to verify the gradient imaging algorithm. The convolutional neural network (CNN) deep learning algorithm is presented to identify the grey-scale map samples preprocessed by the gradient imaging algorithm. The results show that the current perturbation theory clarifies the relationship between the characteristic signal and the morphology of various defects. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bz</i> image reflects the surface morphology of defects. The gradient imaging algorithm can achieve visual detection of defects. The single crack, the irregular crack, and the corrosion can be identified intelligently by the CNN deep learning algorithm. These defects can be evaluated accurately after classification.

Visual Inspection of Irregular Cracks in Steel Structure by Double Gradient Fusion Algorithm Using Alternating Current Field Measurement Technique

The alternating current field measurement (ACFM) is an excellent nondestructive testing method for the inspection of cracks in the steel structure. Generally, the research of the ACFM focus on the inspection of one crack with specific direction. However, the crack may extend to an irregular crack with multi-direction due to the complex stress and the special structural shape. In this paper, the irregular crack was inspected visually without cleaning the coating using the ACFM technique. The distorted electromagnetic field was analyzed around the irregular crack by the finite element method (FEM). The double gradient fusion algorithm (DGFA) was presented to reconstruct the surface morphology of the irregular crack. The ACFM system was developed to inspect the irregular crack under the coating. The results show that the current disturbance effect and the magnetic leakage effect both present at different parts of the irregular crack. The surface morphology of the irregular crack can be inspected visually by the DGFA using the ACFM technique. The visual inspection results give more visible morphology information for the structural health monitoring of the steel structure.

A method to compensate for the lift off effect of ACFM in crack estimation of nonferromagnetic metals

Alternating current field measurement (ACFM) is one of the most important techniques for detecting metal cracks. During measurement, however, the changes in lift-off distance caused by coating corrosion and irregularity of the surface greatly affect the accuracy of crack sizing. To overcome this drawback, an advanced algorithm is proposed in this paper to compensate for the lift-off effect. Benefitting from this method, the dimensions of cracks can be inversed directly without considering the lift-off distance. More importantly, the algorithm has been verified efficiently as estimating the cracks of an aluminum workpiece. The results show that the sizing errors based on this algorithm can be limited to 10%.

Analysis on asymmetrical RCF cracks characterisation using an ACFM sensor and the influence of the rail head profile

Rolling contact fatigue (RCF) cracks are one of the typical surface-breaking defects that impairs the rail service life. RCF cracks propagation can deviate from regular semi-ellipse shapes, turning into asymmetrical profiles. RCF crack measurements using the alternating current field measurement (ACFM) method are unable to accurately characterise asymmetrical cracks, as the sizing algorithm is developed based on regular crack shapes. In this paper, the development of modelling capability to consider asymmetrical RCF cracks and the influence of asymmetrical shapes on ACFM signals are presented. A novel grid scan method using ACFM probe sensor for characterizing asymmetrical crack shapes is proposed. The influences of rail head profile on crack sizing are studied and result shows that it gives maximum 2 mm underestimation in crack depth if the worn rail head profile is not considered in ACFM sizing algorithm.

Alternating Current Field Measurement Probe for Coiled Tubing Inspection of Arbitrary Direction Defects

To reduce the serious accident caused by coiled tubing (CT) defects, the full axial defect detection of CT outer surface based on alternating current field measurement (ACFM) was studied. Combined with the advantages of traditional magnetic flux leakage (MFL) circumferential detection, the arbitrary direction defects detection problem of CT was realized. Currently, the study of the ACFM theoretical model only involves excitation in a single direction, and it is difficult for the probe to provide sufficient magnetic field information in an arbitrary direction for visual inversion of defects. In this work, the magnetic disturbance on the axial defects of CT is analyzed. The characteristic signals of defects recognition are extracted, and the excitation frequency, liftoff height, defects shape, defect depth, and defects cluster are analyzed. The optimal parameters of magnetic field characteristic signal distribution and defects detection on the CT surface are obtained. Finally, a new ACFM array probe is designed for CT 360° (full circumferential) axial defect detection. The results show that the method overcomes the limitation of the directional detection of the single detection technique. The designed ACFM probe can completely evaluate the 360° axial defects of CT.

An Efficient Phenomenological Inversion Method for Reconstruction of Crack Depth Profile in a Metal From ACFM Probe Output Signals

The paper proposes an efficient phenomenological inversion method to determine the depth profile of a surface-breaking crack in a metal from the output signal of an alternating current field measurement (ACFM) probe. The proposed method utilizes a conjugate gradient algorithm to minimize an objective function, representing the difference between probe predicted and actual signals in an iterative manner. The objective function is derived explicitly in terms of crack depth variables by considering a polynomial function for the field distribution in the depth direction and applying appropriate Greens functions. This approach enhances the accuracy and computational efficiency of the inversion process, regardless of the choice of initial crack depth profile or presence of noise in the measurement system. The validity and efficiency of the proposed method are demonstrated by comparing the reconstructed depth profiles of several simulated and machine-made cracks with their actual data, and those obtained using the conventional phenomenological approach based on an efficient stochastic optimization scheme along with a fast pseudoanalytic ACFM probe output simulator.

Reduction of Coil-Crack Angle Sensitivity Effect Using a Novel Flux Feature of ACFM Technique

Alternating current field measurement (ACFM) testing is one of the promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach was proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the center of the excitation coil where the magnetic field is measured. It was found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack followed a sine wave shape. A customized accelerated finite element method solver programmed in MATLAB was adopted to simulate the performance of the designed sensor probe which could significantly improve the computation efficiency due to the small crack perturbation. The experiments were also carried out to validate the simulations. It was found that the ratio between the z and x components of the magnetic field remained stable under various rotation angles. It showed the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique.

Extreme learning machine based sub-surface crack detection and quantification method for ACFM

In this paper, a quantification method for sub-surface crack evaluation based on extreme learning machine (ELM) neural network is proposed to the application of alternating current field measurement (ACFM). According to simulation results of finite element model, the dimensions of cracks can be described by four features of the Bz and Bx curves. An optimized ELM feasible for ACFM detection is trained and validated based on the selected features. Taking the advantage of our proposed sub-surface crack sizing method, the experimental results indicate that the detection and quantification for sub-surface crack is conducted readily. The maximum errors of length and depth predicted by ELM can be limited to 5.65 % and 6.27 %, respectively.