Magnetic Flux Leakage Inspection Research Articles

Numerical Simulation and Analysis on Magnetizing Exciter for Magnetic Flux Leakage

Magnetic flux leakage (MFL) is a non-destructive testing method used to inspect the pipe and magnetization of the pipe wall to saturation is essential for anomalies to be reliably and accurately detected and characterized. Axial components of magnetic flux density obtained during the MFL inspection have been simulated using three-dimensional finite element analysis and the effects of magnetizing exciter parameters on magnetic flux density are investigated. The pipe modeled in this paper has an outer diameter of 127mm (5 in.) with a wall thickness of 9 mm (0.354 in.). According to numerical simulations, an increase in the magnetic flux density of pipe wall is observed with an increase in the permanent magnet length and height. It clearly illustrates that Nd-Fe-B permanent magnet assembly with 70 mm length and 40 mm height may magnetize pipe wall to near saturation.

Temperature Error Compensation New Method of MFL Sensor to Oil-Gas Pipeline Corrosion Inspection

Magnetic flux leakage (MFL) inspection is a common method for detecting inner corrosions of oil-gas pipelines; the Hall Effect element is the core sensor of MFL inspections. The Hall sensor is sensitive to temperature, so environmental changes will lead to output error of Hall sensors. In order to compensate for temperature errors of Hall sensors, a segment of oil-gas pipeline with diameter 6 inch was taken as a research object. A fusion model including 50 Hall sensors and 1 temperature sensor was built up, and a functional link artificial neural network optimized by the artificial immune algorithm was used to fuse the output data of the multiple sensors. The lab research results show that the artificial immune algorithm can improve training speed and precision of the functional link neural network; the model built up can compensate effectively for temperature errors of Hall sensors and under the conditions of changing temperature, the precision of magnetic flux inspections of pipelines to detect corrosions can be improved significantly.

Effects of Magnetic Concentrator on MFL Signals Using FEA

Magnetic flux leakage (MFL) is a non-destructive testing method used to inspect ferrous materials. However, there are a variety of factors that can affect the MFL inspection tool’s ability to detect and characterize anomalies. MFL signals obtained during the inspection of pipes have been simulated using 3D finite element analysis (FEA) and the effects of magnetic concentrator on MFL signals are investigated. Measurements of the leakage flux with various defect depths or widths indicate that the axial component of MFL are improved by magnetic concentrator with the result that significant advantages could be obtained in defect detection schemes, in that the signal to noise ratio (SNR) of MFL signals can be improved by magnetic concentrator.

3D Finite Element Analysis for Magnetic Flux Leakage Testing

Magnetic flux leakage (MFL) is a non-destructive testing method used to inspect ferrous materials. However, apparatus parameters could affect the MFL inspection tool’s ability to characterize anomalies. In this paper, MFL signals obtained during the inspection of pipes have been simulated using three-dimensional finite element analysis and the effects of magnet assembly on MFL signals are investigated. According to numerical simulations, an increase in the leakage flux amplitude is observed with an increase in the permanent magnet size and the inflexion point may indicate the presence of magnetizing pipe wall to near saturation. It clearly illustrates degradation in the MFL with increasing backing iron length. The relationship between MFL apparatus parameters and MFL signals could be utilized in the MFL technique to characterize the defect.

Data Fusion for MFL Signal Characterization

The objective of data fusion is to be able to draw inferences that may not be feasible with data from a single sensor alone. In this paper, data from three sets of sensors are fused to estimate the defect profile from magnetic flux leakage (MFL) inspection data. The three sensors measure the axial, radial and tangential components of the MFL field. Data is fused at the feature level. Examples of signal features are amplitude, width, etc. A radial basis function network (RBFN) is then employed to map the fused features appropriately to obtain the geometric profile of the defect. The feasibility of the approach is evaluated using the data obtained from the MFL inspection of oil pipes. The results obtained by fusing the axial, radial and tangential components appear to be better than those obtained using the axial and radial component alone.

Effects of Apparatus Parameters on MFL Signals Using Orthogonal Experimental Design

Magnetic flux leakage (MFL) is a non-destructive testing method used to inspect ferrous materials. However, there are a variety of factors that can affect the MFL inspection tool’s ability to detect and characterize anomalies. An orthogonal experimental design (OED) method is applied to study the effects of apparatus parameters on MFL signals. Integration of OED method of analysis into a routine sample preparation technique could improve the repeatability and quantization capabilities of MFL tools. Three key apparatus parameters, namely permanent magnet (PM) height, magnetic concentrator (MC) length and backing iron (BI) length are chosen for the present study. The importance of each of these key parameters on MFL signals for different defects is determined by a series of experiments.

Intelligent Monitoring Approach for Pipeline Defect Detection from MFL Inspection

Artificial Neural Networks(ANNS) have top level of capability to progress the estimation of cracks in metal tubes. The aim of this paper is to propose an algorithm to identify modeled cracks by magnetic flux leakage inspection in Non Destructive Testing (NDT) [1, 2, 3, 4, 5, and 6]. The analysis is carried out with a simulated database of signals in which the depth of the crack, its width, shape, And geometric dimension of the detection process, is allowed to change. The simulated signal is input to the network, after a reduction process in which the main features of the signal are extracted. Feature extractors are used in pattern recognition area due to their advantages in representing data. With this approach classifier’s job became easier and more effective. The main goal of the feature extractor is to reflect the characteristics of an object in a given dataset. In this way feature extractor simplify the amount of resources required to describe a large dataset accurately. This paper presents the results of employing different kinds of feature extraction functions and classification and provides compression between them. As the output of ANN, we shall justify if any care in meta lto indicate whether the input signal is crack or not. The analysis based on the neural network and feature extractor functions is shown to be quite top probability of detection.

The Use of Finite-Element-Calculated Magnetic-Flux-Leakage Signals To Study the Characterization of Defects in Steel Coiled Tubing

Summary A primary consideration with coiled tubing (CT) is that it is consumed by fatigue loading during routine operations. Also, rugged oilfield conditions routinely lead to corrosion and other mechanical surface damage. Since fatigue is a surface phenomenon, the presence of a surface imperfection has a significant influence on fatigue-damage mechanisms. This paper describes the study of magnetic-flux-leakage (MFL) inspection signals caused by surface defects in the form of milled circumferential grooves in steel CT. The focus of the investigation is to identify and estimate the size of surface defects on the basis of characteristic MFL signal features. It is demonstrated that this effort is greatly enhanced by finite-element analysis (FEA). The ultimate objective is to extract surface-flaw dimensions accurately from conventional MFL signals. These dimensions are used in computer CT life-prediction models. An axisymmetric FEA model is developed and used to calculate leakage flux density solutions for milled circular and rectangular shaped grooves in 1.75-in.-outside-diameter (OD), 0.156-in.-wall-thickness (WT), 90-ksi CT samples. FEA results are compared to axial and radial MFL signals measured with an experimental inspection unit. Favorable agreement is observed between experimental and FEA data. Furthermore, signal features are correlated with the known slot geometries to identify basic geometry-recognition patterns for different circumferential grooves. Signal features reveal qualitative and quantitative trends relative to surface-flaw dimensional characteristics. The need persists to make the operator's string-management decision-making process more reliable and automatic with respect to determining fatigue life expectancy. The obstacle here is that because of the inherent inaccuracies in commonly used MFL inspection techniques, reliable real-time flaw-evaluation and -characterization capability is limited.

Numerical simulation and experiments of magnetic flux leakage inspection in pipeline steel

The magnetic flux leakage (MFL) method is currently the most commonly used pipeline inspection technique. In this paper, numerical simulation and experimental investigation on defect inspection in pipeline steel using MFL were carried out. In theoretical analysis, typical three-dimensional (3D) defects were accurately modeled and detailed MFL signals in the test surface were calculated by 3D finite element method (FEM). To confirm the 3D FEM results, different artificial defects were made and the MFL experiments were performed. The experimental study demonstrated that the results were agreement with the 3D FEM result. The results show that the 3D FEM is an effective analysis method for pipeline steel MFL inspection.

Data fusion in automated robotic inspection systems

Teams of small modular inspection vehicles for automated inspection tasks offer the possibility of employing a variety of different NDE inspection methods simultaneously. By synergistically utilising information derived from multiple sources, individual deficiencies and limitations can be partially compensated, leading to a more accurate and precise evaluation of the condition of the engineering structure under test. This paper presents approaches based on fusion of NDE data that have been obtained by a heterogeneous team of small inspection robots which are equipped with payloads for magnetic flux leakage, eddy current and ultrasonic inspection. Any potential uncertainties in individual measurements regarding the location of defects constitute the basis for fusion methods based on statistical and probabilistic algorithms. Images of a two-dimensional test structure have been constructed from data derived from different scans, indicating the positions of detected artificial defects. Applying the Dempster-Shqfer theory of evidence and Bayesian analysis, the confidence level in the accuracy of these images is increased and the uncertainty reduced.

Experimental Study of Interference Factors and Finite Element Simulation on Oil-Gas Pipeline Magnetic Flux Leakage Density

Some methods of enhancing oil-gas pipeline magnetic flux leakage (MFL) detection technique are introduced in the paper. Some man-made defects or imperfections on the pipe surface are detected via the axial magnetization inspection vehicle along the pipeline. The magnetic dipole model of corrosion defect is stated and the important interference factors on magnetic flux leakage are analyzed. Finite element method is used to analyze and simulate normal defects on pipe surface, which can attribute to get natural defect MFL signal. The influence of benign pipeline artifacts (valves, welds, tees, flanges, etc.), pipe material and pipe wall, vehicle velocity, defect dimensions and interaction among defects, and so on are studied in detail. The magnetic flux leakage contour images or indication extraction maps are given and presented. These interference factors are compensated and solved. These approaches and results applied in the paper are contributed to the feature extraction and indication of pipeline abnormality. The results suggest that these approaches and conclusions are significantly effective for the pipeline magnetic flux leakage inspection.

LS-SVMs-based reconstruction of 3-D defect profile from magnetic flux leakage signals

Magnetic flux leakage techniques are used extensively to detect and characterise defects in natural gas and oil transmission pipelines. Based on the least squares support vector machines (LS-SVMs) technique, this paper presents a novel approach for the three-dimensional (3-D) defect profile reconstructed from magnetic flux leakage signals. The basic theory of LS-SVM for function estimates is given. The hyper-parameters of the LS-SVMs problem formulations are tuned using a 10-fold cross validation procedure and a grid search mechanism, and applying the pruning algorithm to impose sparseness on the LS-SVMs. The training data are composed of the measured and simulated data. A mapping from MFL signals to 3-D profiles of defects is established, the reconstruction of 3-D profiles of defects from magnetic flux leakage inspection signals is achieved and 3-D error of reconstruction results is analysed. The experimental results show that the LS-SVM has high precision, good generalisation ability and capability of tolerating noise.

Optimization of the magnetic circuit in the MFL inspection system for storage-tank floors

Magnetization is the key to inspection of a tank floor via the magnetic-flux-leakage (MFL) technique. In order to optimize the magnetic circuit of the MFL detector and obtain the best detection effects, the influences of the magnet size on the floor magnetization condition, the gap magnetic flux density, and the magnetic force were studied with the help of the finite element method (FEM) and the effects of some other parameters, such as the magnet pole spacing and pole-piece thickness, on the signal-to-noise ratio were analyzed. The simulation results indicate that variation of the magnet width affects the magnetization much more than variation of the magnet thickness and that the detector can reach a trade-off between the magnetization effects and the driving force when the magnet is about 30 mm thick and 40 mm wide. On condition that the floor has reached its magnetizing saturation, an increase in the magnet-pole spacing and the pole-piece thickness can improve the testing sensitivity and the signal-to-noise ratio.

Corrosion Failure of Bottom Plates of an Aboveground Storage Tank

This paper describes the investigation of a corrosion failure of bottom plates on an aboveground tank used for the storage of potable water. The tank was internally inspected for the first time after six years of service. Paint blisters and rust spots were observed on the bottom plates and first to third course shell plates. Sand blasting and repainting of the bottom plates and first course shell plates was to be used as a remedial measure. However, during the sand blasting, holes and deep pitting were observed on the bottom plates. On-site visual inspection, magnetic flux leakage (MFL) inspection, ultrasonic testing (UT), and evaluation of the external cathodic protection (CP) system were used in the failure analysis. The corrosion products were analyzed using energy-dispersive X-ray analysis (EDAX). The failure is attributed to the ingress of water and its impoundment under the tank bottom along the periphery inside the ring wall and failure of water side epoxy coating. Various measures to prevent such failures in the future are recommended.

An adaptive method for channel equalization in MFL inspection

Influenced by the lift-off value between the pipeline and coil sensors, the various properties of electronic component and the different location of coil sensor, the output signal's amplitude and phase of each channel are different despite the same flaw in magnetic flux leakage (MFL) inspection. The channel-to-channel mismatch may severely degrade the performance of testing equipment and disturb the evaluation of the level of flaw unless some form of compensation is employed. In this paper an adaptive method for channel equalization in MFL inspection is presented by using the finite impulse response filter. Both theoretical analysis and experimental results have shown the effectiveness of the proposed method.

Factors Affecting Magnetic Flux Leakage Inspection of Tailor-Welded Blanks

The development of a laboratory-based tailor-welded blank (TWB) inspection system using the principles of magnetic flux leakage (MFL) is presented. The effects of variations in inspection system operating parameters are quantified to allow for optimized system performance. The parameters examined include the applied magnetic field strength, inspection scanning velocity, spatial resolution of acquired signals, specimen end effects, and pole piece liftoff. The results indicate that this inspection method is relatively insensitive to operating parameters and is therefore robust.

The effect of the defect location on the finite element modelling of defect MFL fields

Magnetic flux leakage (MFL) measurement is one of the most widely used non-destructive testing techniques for in-service inspection of oil & gas pipelines. Numerical computation techniques, such as the finite element method (FEM), have been successfully employed for the study of flux leakage fields. FEM has special advantages when predicting the field profiles of simulated corrosion defects because it can model complex boundary geometries and non-linear material characteristics. According to the traditional FEM modelling method, the defect should be placed at different locations along the pipe to simulate the movement of the MFL inspection tool inside the pipe. In this paper, the effect of defect location within the finite element model is discussed and a simplified FEM modelling method is introduced. The simulation results obtained from the new method are compared to those of the traditional method. It is found that the new method can significantly shorten the solution times without compromising the accuracy of the FEM model of MFL inspection.

Defect separation considerations in magnetic flux leakage inspection

The magnetic flux leakage (MFL) method is used for assessing the corrosion integrity of oil and gas pipelines. The interpretation of defect-induced magnetic signals has to take into account the inspection tool and pipeline steel characteristics. The MFL signals are very sensitive to the stress state and magnetisation level of the specimen. The presence of a material-loss defect influences both the stress and magnetisation distributions in the material. Moreover, clustering of the corrosion defects complicates this situation by superposition of the local stresses and by magnetic shielding in the region between defects. In this paper, the degree of interaction between two adjacent metal-loss defects is analysed as a function of the defects' centre-to-centre separation, residual and applied stresses, and magnetic flux density. The necessary centre-to-centre separation for two defects to be considered interacting is evaluated by extrapolation of the magnetic signal features of known defect geometries.

Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection

Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented.

The Design of a Mechanical Damage Inspection Tool Using Dual Field Magnetic Flux Leakage Technology

This paper describes the design of a new magnetic flux leakage (MFL) inspection tool that performs an inline inspection to detect and characterize both metal loss and mechanical damage defects. An inspection tool that couples mechanical damage assessment as part of a routine corrosion inspection is expected to have considerably better prospects for application in the pipeline industry than a tool that complicates existing procedures. The design is based on study results that show it is feasible to detect and assess mechanical damage by applying a low magnetic field level in addition to the high magnetic field employed by most inspection tools. Nearly all commercially available MFL tools use high magnetic fields to detect and size metal loss such as corrosion. A lower field than is commonly applied for detecting metal loss is appropriate for detecting mechanical damage, such as the metallurgical changes caused by impacts from excavation equipment. The lower field is needed to counter the saturation effect of the high magnetic field, which masks and diminishes important components of the signal associated with mechanical damage. Finite element modeling was used in the design effort and the results have shown that a single magnetizer with three poles is the most effective design. Furthermore, it was found that for the three-pole system the high magnetization pole must be in the center, which was an unexpected result. The three-pole design has mechanical advantages, including a magnetic null in the backing bar, which enables installation of a pivot point for articulation of the tool through bends and restrictions. This design was prototyped and tested at Battelle’s Pipeline Simulation Facility (West Jefferson, OH). The signals were nearly identical to results acquired with a single magnetizer reconfigured between tests to attain the appropriate high and low field levels.