Deep neural network for simulation of magnetic flux leakage testing

“…Deep neural networks (DNN) can learn meaningful structures of data with large training datasets. In [11], a DL model with a Ushape structure was designed to nonlinearly map the input geometry of the MFLT system to the distribution of magnetic field around cracks. Single-image super-resolution, a fundamental challenge in computer vision, has received considerable attention, with researchers establishing end-to-end DNN structures to rebuild HRIs from LRIs [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development of magnetic image super-resolution model for nondestructive testing

Le,

Luong,

2023

Meas. Sci. Technol.

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Electromagnetic testing (ET) is a method in the nondestructive evaluation (NDE) methodology for detecting and evaluating cracks in engineering structures by measuring the distribution of the electromagnetic field. With the development of advanced deep learning (DL) techniques, DL is getting attention in crack detection problems through the measuring signals of NDE systems. However, obtaining the necessary big data for such experiments is a time-consuming and expensive task. Moreover, the quality of the crack detection results heavily depends on the quality and spatial resolution of the input image data. In this research, we solve the above-mentioned problems by proposing a DL-based method on the magnetic image for both the image super-resolution and image denoise. We first build a magnetic image simulation framework for the ET system, and thus, it is possible to build a large dataset for training the DL model. Second, we build a DL model for enhancing the quality of the magnetic image in both the spatial resolution and the denoise signal. This approach helps to reduce the cost of experiments (i.e. reducing the number of sensors for each experiment), sensor fabrication process, and noise removal of the ET system. We evaluate the proposed approach on several cracks with different sizes and shapes of aluminum specimens. The evaluation metrics, such as the structural similarity index measure (SSIM) and root mean square error show good performance of the proposed approach and compared to traditional methods.

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“…A theoretical model was applied to solve stress concentration problems in demonstrating the feasibility of the model in early diagnosis. Le [ 37 ] proposed a method of simulating the magnetic field distribution in magnetic flux leakage detection using a deep neural network. In terms of quantitative characterization of corrosion morphology, Xu [ 38 ] developed an indirect method for describing the corrosion morphology by injecting 60 mL of red ink into the cracked sheath of the old cable removed from a bridge and observing the distribution of the red ink within the cable body after two days.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of the Magnetic Leakage Detection of a Corroded Steel Strand Due to Vibration

Zhang

Ding

Yuan

et al. 2023

Sensors

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The self-magnetic flux leakage (SMFL) detection technique has great potential in the corrosion detection of bridge stay cables due to its advantages of small testing equipment, high accuracy, and fast testing rate. However, the vibration effect in the cable’s SMFL detection is unclear. To address this, the influence of vibration on the magnetic field distribution of cable structure is analyzed theoretically. According to the theoretical model, the effect of vibration on SMFL detection primarily manifests as displacement changes (displacement-added magnetic field) and defect shape changes (deformation-added magnetic field). SMFL detection experiments are conducted on steel strands. The results demonstrate that the displacement-added magnetic field exhibits statistical characteristics in the form of a normal distribution, fluctuating around the zero value. The impact of the deformation-added magnetic field on SMFL is linearly correlated with the corrosion ratio c. Moreover, a corrosion characterization index A was proposed and has an excellent linear fit with the corrosion ratio c. The index A effectively improves the accuracy of corrosion detection and provides early warning for the maintenance of cable structures.

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Deep neural network for simulation of magnetic flux leakage testing

Cited by 15 publications

References 14 publications

Fast Magnetic Field Approximation Method for Simulation of Coaxial Magnetic Gears Using AI

Fast Magnetic Field Approximation Method for Simulation of Coaxial Magnetic Gears Using AI

Development of magnetic image super-resolution model for nondestructive testing

Experimental Analysis of the Magnetic Leakage Detection of a Corroded Steel Strand Due to Vibration

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