Experimental and simulation methods to study the Magnetic Tomography Method (MTM) for pipe defect detection

Li, Zhichao; Jarvis, Rollo; Nagy, Péter B.; Dixon, Steve; Cawley, P.

doi:10.1016/j.ndteint.2017.07.018

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…In reality, ferromagnetic materials are affected by the magnetic field of the Earth from the beginning of their production process. There may also be some unknown external magnetic sources in the surrounding environment that affect the magnetic behavior of ferromagnetic objects [59]. However, as accurately as possible, we can apply the magnetic field of the Earth to the object and simulate its magnetic behavior, although some divergence will exist between the simulation and the experimental results.…”

Section: Simulations and Resultsmentioning

confidence: 99%

Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method

2018

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Reinforced concrete is the most commonly used material in urban, road, and industrial structures. Quantifying the condition of the reinforcing steel can help manage the human and financial risks that arise from unexpected reinforced concrete structure functional failure. Also, a quantitative time history of reinforcing steel condition can be used to make decisions on rehabilitation, decommissioning, or replacement. The self-magnetic behavior of ferromagnetic materials is useful for quantitative condition assessment. In this study, a ferromagnetic rebar with artificial defects was scanned by a three-dimensional (3D) laser scanner. The obtained point cloud was imported as a real geometry to a finite element software platform; its self-magnetic behavior was then simulated under the influence of Earth’s magnetic field. The various passive magnetic parameters that can be measured were reviewed for different conditions. Statistical studies showed that 0.76% of the simulation-obtained data of the rebar surface was related to the defect locations. Additionally, acceptable coincidences were confirmed between the magnetic properties from numerical simulation and from experimental outputs, most noticeably at hole locations.

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Section: Simulations and Resultsmentioning

confidence: 99%

Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method

2018

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“…The fitness of each salp is then calculated to seek the best fitness, and its location is defined as the food source (F). During this period, the coefficient c 1 is updated utilizing Equation 14, and the location of leader salp is updated utilizing Equation (13). In addition, the locations of follower salps are updated utilizing Equation (16).…”

Section: Salp Swarm Algorithmmentioning

confidence: 99%

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

et al. 2019

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During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

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“…The magnetization can be achieved in three different ways, namely, by excitation coil, a permanent magnet, and the composite magnetic flux method [17][18][19]. The flux leakage is detected with the help of Hall, coil, and magnetoresistance (MR) sensors, respectively [20][21][22]. From the group of MR sensors, giant magnetoresistance (GMR) has gained many advantages over the other sensors in terms of sensitivity, compactness, and cost [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Leakage in a Sustainable Water Pipeline Based on a Magnetic Flux Leakage Technique

et al. 2022

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Pipelines are typically used to transport oil, natural gas, water, etc. It is one of the most effective methods for transferring fluids over long distances. However, long-term usage of these pipes without maintenance results in the formation of residues, which will pave the way for pipeline accidents and soil contamination. To ensure the safety and protection of resources, these sustainable pipelines need to be inspected to avoid losses. This work aims to investigate various internal defect leaks in the non-uniform thickness of sustainable water pipes that are joined with a pipe expander. The magnetic flux leakage technique was implemented to evaluate these defects by means of a flexible GMR sensor array. An inspection robot containing two units was fabricated with the aid of 3D printing. The power unit provides the necessary thrust to actuate the entire robot whereas the sensing unit is responsible for analyzing the leaks. The robot’s movement is predicted by the MPU6050 and ultrasonic distance sensors that are plotted as motion plots. The sensing unit consists of permanent magnets and a giant magnetoresistance (GMR) array to interrogate the flux leakage in the defect region. The flux leakage from the defects was stored with the help of an Arduino microcontroller, which controls the overall process. In addition, the spring suspension is provided to regulate the motion of the robot. The flux leakage from the defect region was plotted as waveform graphs. Thus, the results are effectively presented and compared. The calculated signal-to-noise ratio (SNR) of the magnetic flux leakages (MFLs) for 4.5 mm-thick pipe defects was 12 to 20.8 dB, and for 6.52 mm-thick pipe defects, it was 9.5 to 19 dB. In sum, the MFL technique provides a reliable method for the sustainable development of water supply to wide areas.

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Experimental and simulation methods to study the Magnetic Tomography Method (MTM) for pipe defect detection

Cited by 20 publications

References 15 publications

Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method

Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

Analysis of Leakage in a Sustainable Water Pipeline Based on a Magnetic Flux Leakage Technique

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