Magnetic Flux Leakage Sensor Research Articles

Development of multi-channel magnetic flux leakage testing system for bridge cables

Magnetic flux leakage (MFL) is one of the most popular techniques for detecting broken wire in bridge cables. MFL sensor array has attracted a lot of attention in recent years due to its capacity to measure the spatial distribution of the magnetic field of bridge cable. More sensors in the array lead to increased useful information extraction from the MFL field, allowing for more precise quantitative evaluation of bridge cable damage. This study reports the development of a cable MFL testing system capable of synchronous acquiring up to 512 channels of MFL sensors signal. Radial sensor arrays (RSA) composed of vertically placed magnetic sensors spaced at equal intervals are developed to be arranged around the bridge cable. They record the spatial distribution of the magnetic leakage field of the broken wire by collecting the MFL signal at various lift-offs. To solve the problem of multi- channel MFL signal synchronous acquisition, this study develops a MFL signal acquisition card based on multiplexing technology, capable of synchronous acquisition of up to 512 channels of MFL signal. Multiplexers are used to switch sensor channels, followed by multi-channel A/D conversion chips to convert the transmitted data. The converted data are temporally stored in the First-In-First-Out (FIFO) buffer of the Field Programmable Gate Array (FPGA) and transferred to the computer through the network port for display, storage, and signal processing. Finally, the motion control circuit, mechanical structure, and control software of the bridge cable MFL testing system are fully customized. Experiment results reveal that the system can be used in multi-channel MFL testing, laying the foundation for further multi-channel signal processing and bridge cable health evaluation.

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Magnetic flux leakage (MFL), a widely used nondestructive evaluation (NDE) method, for inspecting pipelines to prevent potential long-term failures. However, during field testing, uncertainties can affect the accuracy of the inspection and the decision-making process regarding damage conditions. Therefore, it is essential to identify and quantify these uncertainties to ensure the reliability of the inspection. This study focuses on the uncertainties that arise during the inverse NDE process due to the dynamic magnetization process, which is affected by the relative motion of the MFL sensor and the material being tested. Specifically, the study investigates the uncertainties caused by sensing liftoff, which can affect the output signal of the sensing system. Due to the complexity of describing the forward uncertainty propagation process, this study compared two typical machine learning (ML)-based approximate Bayesian inference methods, convolutional neural network and deep ensemble, to address the input uncertainty from the MFL response data. Besides, an autoencoder method is applied to tackle the lack of experimental data for the training model by augmenting the dataset, which is constructed with the pre-trained model based on transfer learning. Prior knowledge learned from large simulated MFL signals can fine-tune the autoencoder model which enhances the subsequent learning process on experimental MFL data with faster generalization. The augmented data from the fine-tuned autoencoder is further applied for ML-based defect size classification. This study conducted prediction accuracy and uncertainty analysis with calibration, which can evaluate the prediction performance and reveal the relation between the liftoff uncertainty and prediction accuracy. Further, to strengthen the trustworthiness of the prediction results, the decision-making process guided by uncertainty is applied to provide valuable insights into the reliability of the final prediction results. Overall, the proposed framework for uncertainty quantification offers valuable insights into the assessment of reliability in MFL-based decision-making and inverse problems.

A Novel Cascaded Deep Learning Model for the Detection and Quantification of Defects in Pipelines via Magnetic Flux Leakage Signals

In this paper, we present a machine learning based quantitative method for the interpretation of signals gathered from non-destructive-testing (NDT) of steel pipelines via a semi-autonomous in-line-inspection (ILI) robot. The robot has a magnetic-flux-leakage (MFL) sensor that produces three axis data for each point of pipeline with specific intervals. Both the robot and the MFL sensor have been developed in-house. The signals collected via MFL sensor are converted into images to be used as input for the proposed defect detection model. We propose a combination of a defect detection model based on SwinYv5 object detection algorithm and a quantification model based on Cross-Residual Convolutional Neural Network (CR-CNN). The detected defect locations are used to extract the Region of Interest (ROI) images of defects that are used as input for the quantification model. In data collection phase, numerous tests have been conducted via a special test mechanism and a custom data augmentation technique has been deployed in order to increase the amount and variety of training data. According to test results, the proposed method is capable of detecting defects with a precision of 98.9% and quantifying them with maximum errors of 1.30, 1.65 and 0.47 mm for length, width and depth respectively.

Mechanism of Magnetic Flux Leakage Detection Method Based on the Slotted Ferromagnetic Lift-Off Layer.

Magnetic flux leakage (MFL) testing is widely used in non-destructive testing of ferromagnetic components. In view of the serious attenuation of the leakage magnetic field (LMF) caused by the transmission of LMF in the lift-off layer between the measuring point and the workpiece, this paper introduces an MFL detection method based on the slotted ferromagnetic lift-off layer (SFLL). The conventional non-ferromagnetic lift-off layer is changed to a ferromagnetic lift-off layer with a rectangular slot. The magnetic sensor is fixed above the slot and scans the workpiece together with the lift-off layer. First, the detection mechanism of the new method was studied by an equivalent LMF coil model. The permeability perturbation effect and the magnetization enhancement effect were analyzed in the new method. Based on the detection mechanism, the lift-off tolerance of the new method was investigated. Then, the LMF enhancement and lift-off tolerance of the new method in the steel plate detection model were studied. Finally, experiments were conducted to compare the new method with the conventional method. The simulation and experimental results show that the slotted ferromagnetic lift-off layer enhances the amplitude of the MFL signal and is tolerant to the lift-off value. This method provides a new idea for optimizing the design of the MFL sensor and improving the sensitivity of MFL detection at a large lift-off value.

A Novel Magnetic Flux Leakage Sensor System for Inspecting Large Diameter Pipeline

The magnetic flux leakage (MFL) type non-destructive inspection can be performed only when the metal object is sufficiently magnetically saturated. Therefore, since the general MFL sensor system used for pipeline inspection must sufficiently saturate the pipe, the weight and size of MFL sensor system are inevitably increased according to the diameter or thickness of the pipe. However, if the weight of the MFL sensor system is excessively increased, it is difficult to drive, and thus it is difficult to inspect a pipe having a large diameter. In this paper, the novel MFL sensor system which can be inspected large-diameter pipes without increasing the weight of the MFL sensor system is proposed and compared to the conventional MFL sensor system used for the pipe inspection. Since the proposed MFL sensor system is a system that saturates the pipe locally, it has the advantage of being able to inspect the pipe regardless of the pipe diameter.

In-line Inspection(ILI) 기반 확률론적 구조적 파손 예측

Underground water supply pipelines are prone to functional deterioration and damage because of the occurrence and growth of corrosion over time. Therefore, to ensure the safety of such pipelines, their service life should be accurately predicted using precise inspection information. For this purpose, in-line inspection is applied for diagnosing pipelines. In this study, the structural safety of a water supply pipeline in Korea was evaluated by applying Monte-Carlo simulation, which is a stochastic analysis method, to the corrosion information of large-diameter water pipes obtained through a magnetic flux leakage sensor. For this purpose, the most appropriate probability distribution function and parameters were estimated through the statistical analysis of the data obtained from the sensor, and the corrosion growth was estimated using the probability distribution function determined from the simulation results. The safety factors for the internal pressure and external load for each pipe, which depended on the corrosion growth, could be derived probabilistically. Thus, the structural safety deterioration of the pipe over time could be estimated, and an appropriate rehabilitation method was proposed. In addition, the optimum rehabilitation time could be determined. This study will provide guidelines for the rehabilitation of water supply pipelines.

Theory and Development of Magnetic Flux Leakage Sensor for Flaws Detection: A Review

This paper presents a comprehensive review of the development of magnetic flux leakage (MFL) applied by the researcher to improve existing methodology and evaluation techniques in MFL sensor development for corrosion detection in Above Storage Tanks (ASTs). MFL plays an important role in Non-Destructive (NDT) testing to detect crack and corrosion in ferromagnetic material. The demand for more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to major catastrophic upon questionable signal analysis. The accuracy of the MFL signal is crucial in validating the proposed method used in MFL sensor development. This is because the size, cost, efficiency, and reliability of the overall MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a reliable analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the overall performance of the system. This paper also discusses the advantages and disadvantages of major types of MFL sensors used in NDT based on the working principle and sensitivity on the abrupt signal acquisition. The application of the Artificial Neural Network (ANN) and Finite Element Method (FEM) also discussed to identify the impact on the credibility of the MFL signal.

Improved methods for sizing metal loss in dents for ECA

Dents interacting with metal loss remain as a significant challenge to operators. Existing regulations require that dents with metal loss within high consequence areas be treated as immediate repairs or 60-day conditions, resulting in costly excavations for many operators. At the time when these regulations were written, it was not clear whether inline inspection technologies could discriminate the nature of the metal loss (i.e. corrosion or mechanical damage) or provide accurate sizing. Furthermore, advanced analysis techniques such as finite element analysis were limited, and fitness- forservice evaluations were not common. While the technological hurdles involved with evaluating interacting dent and metal loss features have been overcome, sensor lift-off remains a challenging issue for magnetic flux leakage (MFL) inspection tools, as sizing accuracy degrades at larger lift-off distances. Until recently, the sensor lift-off issue limited the ability to perform fitness- for- service evaluations because the metal loss in dent features could not be confidently sized. This study demonstrates how integrated lift-off sensors can be used to quantify the lift-off as the MFL sensors pass over a dent. This technology integration has allowed the confident application of sizing specifications for many dents with metal loss, thereby permitting robust fitness- for- service evaluations. Several case studies are examined in this paper, demonstrating how the integrated MFL and lift-off technology can serve to reduce excavations while still ensuring safe pipeline operations.

Corrosion Sensors for Structural Health Monitoring of Oil and Natural Gas Infrastructure: A Review

Corrosion has been a great concern in the oil and natural gas industry costing billions of dollars annually in the U.S. The ability to monitor corrosion online before structural integrity is compromised can have a significant impact on preventing catastrophic events resulting from corrosion. This article critically reviews conventional corrosion sensors and emerging sensor technologies in terms of sensing principles, sensor designs, advantages, and limitations. Conventional corrosion sensors encompass corrosion coupons, electrical resistance probes, electrochemical sensors, ultrasonic testing sensors, magnetic flux leakage sensors, electromagnetic sensors, and in-line inspection tools. Emerging sensor technologies highlight optical fiber sensors (point, quasi-distributed, distributed) and passive wireless sensors such as passive radio-frequency identification sensors and surface acoustic wave sensors. Emerging sensors show great potential in continuous real-time in-situ monitoring of oil and natural gas infrastructure. Distributed chemical sensing is emphasized based on recent studies as a promising method to detect early corrosion onset and monitor corrosive environments for corrosion mitigation management. Additionally, challenges are discussed including durability and stability in extreme and harsh conditions such as high temperature high pressure in subsurface wellbores.

Theory and development of magnetic flux leakage sensor for flaws detection: A review

<p>This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece. </p>

A Sensor for Broken Wire Detection of Steel Wire Ropes Based on the Magnetic Concentrating Principle.

Electromagnetic testing is the most widely used technique for the inspection of steel wire ropes. As one of the electromagnetic detecting approaches, the magnetic flux leakage (MFL) method has the best effect for the detection of broken wires. However, existing sensors based on MFL method still have some problems. (1) The size of the permanent magnet exciter is usually designed according to experience or rough calculation, and there is not enough depth analysis for its excitation performance; (2) Since the detectable angular range for a single Hall component is limited, Hall sensor arrays are often employed in the design of MFL sensors, which will increase the complexity of the subsequent signal processing due to the extensive use of Hall components; (3) Although the new magneto-resistance sensor has higher sensitivity, it is difficult to be applied in practice because of the requirement of the micron-level lift-off. To solve these problems, a sensor for the detection of broken wires of steel wire ropes based on the principle of magnetic concentration is developed. A circumferential multi-circuit permanent magnet exciter (CMPME) is employed to magnetize the wire rope to saturation. The traditional Hall sensor array is replaced by a magnetic concentrator to collect MFL. The structural parameters of the CMPME are optimized and the performance of the magnetic concentrator is analyzed by the finite element method. Finally, the effectiveness of the designed sensor is verified by wire breaking experiment. 1–5 external broken wires, handcrafted on the wire rope with a diameter of 24 mm, can be clearly identified, which shows great potential for the inspection of steel wire ropes.

Magnetic field gradient as the most useful signal for detection of flaws using MFL technique

Abstract The magnetic flux leakage (MFL) technique is extensively used for detection of flaws as well as for evaluation of their dimensions in ferromagnetic materials. However, proper analysis of the MFL signal is hindered by the MFL sensor velocity causing distortions of this signal. Traditionally measured components of the MFL signal are particularly sensitive to the scanning velocity. In this paper, an another signal – the gradient of the normal component of magnetic flux density – was proposed as it is less sensitive to the scanning velocity. Results obtained for scans of the steel plate with artificially manufactured flaws confirm this statement.

A New Design of MFL Sensors for Self-Driving NDT Robot to Avoid Getting Stuck in Curved Underground Pipelines

The magnetic flux leakage (MFL) type pipeline inspection gauge (PIG) is commonly used to detect defects on the underground pipelines as one of the nondestructive testing instruments. Although it is effective for the inter-city scale large size pipeline, it is hard to detect defects for the small size pipeline installed inside the city. So, it is necessary to adopt self-driving robot system to drive PIG module because the small gas pressure inside a small size pipeline is not enough to push PIG module. Even though it is enough to pass in straight line, when this module passes through bent pipe or welded pipe joint, the magnetic force between pipe and inspection module is increased sharply so that the PIG module could be stuck eventually. This paper proposed a new design structure of MFL system to decrease stuck forces on the pipe wall. All structural parameters for MFL sensors based on the magnetic field theory were designed to minimize the magnetic force and maximize the detection sensitivity of defect signals. The performance of the proposed system was verified by simulation results using 3-D finite-element method and experimental measured ones.

Determination Scheme for Accurate Defect Depth in Underground Pipeline Inspection by Using Magnetic Flux Leakage Sensors

The magnetic flux leakage-type nondestructive testing has been applied for the highly efficient inspection of defects in ferromagnetic materials such as underground gas pipelines. In the system, the magnetic field is applied to magnetize a steel pipe so as to induce the leakage signal in the vicinity of defects on the pipe. From the measured sensor signals, decomposing or estimating sizes and shapes of defects are necessary. For the maintenance, the depth estimation is the most important procedure for the management of a safety accident. However, the previous depth estimation contains high error rate inevitably because the depth signal implies the width and length error simultaneously. This paper focused on the enhanced algorithm for the depth sizing in various defects by using the decoupling algorithm to enhance the accuracy of defect depth. Estimated results show good agreements with measured ones.

A New Micro Magnetic Bridge Probe in Magnetic Flux Leakage for Detecting Micro-cracks

Magnetic flux leakage (MFL) testing has been widely used as an efficient non-destructive testing method to detect damage in ferromagnetic materials. It’s of great importance to improve the testing capability of MFL sensors. In this paper, a micro magnetic bridge method in MFL of high sensitivity is proposed to detect micro-cracks. This method consists of a micro magnetic bridge core and an induction coil. Furthermore, a novel micro magnetic bridge probe (MMBP) of higher spatial resolution is designed and developed with $$10~\upmu \hbox {m}$$ width between the two sides of this MMBP in the testing magnetic bridge. The lift-off effect of this new MMBP is studied via finite element method and experimental verification. The results show this MMBP can achieve high sensitivity only when working with a micro-lift-off value. To examine the detecting capability of this MMBP, micro-cracks in magnetic particle inspection sensitivity testing pieces are all inspected, and the lowest depth value is only $$7~\upmu \hbox {m}$$ . The MMBP in this paper improves the testing capability of MFL to the micrometre scale and can be widely used to detect grinding micro-cracks in bearing rings.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Effects of excitation system on the performance of magnetic-flux-leakage-type non-destructive testing

The effects of excitation system on the performance of magnetic flux leakage (MFL)-type non-destructive testing were investigated. The investigation employed parameterized finite element analysis of the MFL testing of ferromagnetic samples to reveal the effects of the structural design and parameters of the excitation system, including the geometry and dimensions of the yoke, lengths and positions of coils and thickness of the shielding layer. Moreover, a parameter optimization method, combining parameterized finite element analysis with a genetic algorithm, was proposed to optimize the dimensions of the yoke and thus obtain maximum magnetic flux leakage. Simulation and optimization results were used to propose an MFL sensor with a preferred structural design and preferred parameters of the excitation system. Comparative experiments were conducted to validate the numerical simulation results. The experimental results agreed well with the results of numerical simulation, and the superior performance of the MFL sensor with the preferred excitation system was validated.

Magnetic flux leakage–based local damage detection and quantification for steel wire rope non-destructive evaluation

A magnetic flux leakage method was applied to detect damage when inspecting steel wire rope. A multi-channel magnetic flux leakage sensor head was fabricated using Hall sensors and permanent magnets to adapt to the wire rope. Three types of artificial damage were created on a wire rope specimen. The magnetic flux leakage sensor head scanned the damaged specimen to measure the magnetic flux density while the damage was expanding in three steps. Signal processing processes including the enveloping process based on Hilbert transform were performed to clarify the flux leakage signal due to the damage. The enveloped signals were then analyzed for objective damage detection by comparing with the threshold value. For improvement of quantitative analysis, three types of new damage indexes that utilize the relationship between the enveloped magnetic flux leakage signal and the threshold value were additionally proposed. By using the proposed damage indexes and the general damage indexes for the magnetic flux leakage method, the detected magnetic flux leakage signals from each damage type were quantified. The trends of the extracted damage indexes according to damage levels were analyzed to examine the applicability and reliability of the proposed damage indexes for the magnetic flux leakage based wire rope inspection.

Improvement of MFL Sensing based Damage Detection and Quantification for Steel Bar NDE

A magnetic flux leakage (MFL) method was applied to detect damage for inspecting the steel bar. A multi-channel MFL sensor head was fabricated using Hall sensors and permanent magnets. The MFL sensor head scanned the damaged specimen that have 5 levels of depth to measure the magnetic flux density. A signal processing process including the enveloping process based on Hilbert transform was performed to clarify the flux leakage signal. The enveloped signals were then analyzed for objective damage detection by comparing with the threshold value. For improvement of quantitative analysis, new damage indexes that utilize the relationship between the enveloped MFL signal and the threshold value were additionally proposed. By using the proposed damage indexes and the general damage index for the MFL method, the detected MFL signals were quantified and analyzed according to the size of damage increase.

A Lift-Off-Tolerant Magnetic Flux Leakage Testing Method for Drill Pipes at Wellhead.

To meet the great needs for MFL (magnetic flux leakage) inspection of drill pipes at wellheads, a lift-off-tolerant MFL testing method is proposed and investigated in this paper. Firstly, a Helmholtz coil magnetization method and the whole MFL testing scheme are proposed. Then, based on the magnetic field focusing effect of ferrite cores, a lift-off-tolerant MFL sensor is developed and tested. It shows high sensitivity at a lift-off distance of 5.0 mm. Further, the follow-up high repeatability MFL probing system is designed and manufactured, which was embedded with the developed sensors. It can track the swing movement of drill pipes and allow the pipe ends to pass smoothly. Finally, the developed system is employed in a drilling field for drill pipe inspection. Test results show that the proposed method can fulfill the requirements for drill pipe inspection at wellheads, which is of great importance in drill pipe safety.