Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

Peng, Xiang; Anyaoha, Uchenna; Liu, Zheng; Tsukada, Kazuhiko

doi:10.1109/tmag.2020.2981450

Cited by 97 publications

(19 citation statements)

References 123 publications

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“…Despite their potential, these techniques face challenges such as acoustic coupling, required accessibility to structures, and low signal-to-noise ratios in highly attenuating materials. Examples of conventional NDT techniques are Visual Inspection (VI) [ 85 , 86 ], Infrared Testing (IR) [ 87 ], Acoustic Emission Testing (AE) [ 88 ], Electromagnetic Testing (ET) [ 89 ], Liquid Penetrant Testing (PT) [ 90 , 91 ], Radiographic Testing (RT) [ 92 ], Magnetic Particle Testing (MPT) [ 93 ], Ultrasonic Testing (UT) [ 94 , 95 , 96 ], Film Radiography (FR) [ 97 ], Eddy Current Testing (ECT) [ 98 ], Straight Beam Ultrasonic Testing [ 99 ], Leak Testing (LT) [ 100 ], and Magnetic Flux Leakage (MFL) [ 101 ].…”

Section: Ndt Systemsmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

126

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This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

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Section: Ndt Systemsmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

126

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“…A similar task was approached using ultrasonic phased array in work [16]. Also, Magnetic-flux leakage-based pipeline corrosion assessment is studied in work [17] which includes adopting ML methods to predict future corrosion status.…”

Section: ) Shm-based Pipe Corrosion Monitoringmentioning

confidence: 99%

Total Unwrapped Phase-Based Diagnosis of Wall Thinning in Nuclear Power Plants Secondary Piping Structures

et al. 2022

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Inspection-based techniques are being widely used for aging management of passive structures in nuclear power plants (NPPs). This paper presents a condition-based maintenance approach to detect corrosion in NPPs secondary piping structures using distributional features extracted from the total unwrapped phase signal of accelerometers. To demonstrate a condition-based maintenance approach, a scaled-down experimental testbed and data-driven methodology are developed and validated for detecting surrogate corrosion processes in a piping structure. The surrogate corrosion process is emulated by removing different levels of mass from the pipe-bend section and measuring the vibration of the pipe bend using five tri-axial accelerometers. The data collected are processed using the Hilbert-Huang transform. Distributional features of the total unwrapped phase from each accelerometer are used to develop a binary classifier to predict a degraded pipe against baseline pipes (healthy state). The binary classification model is extended to a multiclass classification to predict the level of mass removal. Binary and multiclass classification are performed using traditional machine learning algorithms such as logistic regression, neural networks, random forest, and support vector machine. All the algorithms achieved at least 99% prediction accuracy in each direction of the tri-axial accelerometer and significantly outperformed intrinsic mode function-based prediction methods. Performance of classification algorithms were evaluated using data for different number of the tri-axial accelerometers, including a single sensor. The overall prediction accuracy for all these cases was over 99%. Also, the models were interpreted using Shapley additive explanation values to understand the contribution of each input feature in diagnosing the percentage of mass removal. Finally, the performance of a transfer learning model is demonstrated to adopt a machine learning model on to an another elbow, and the prediction performance over 99% is achieved with partial retraining.INDEX TERMS Hilbert-Huang transform, corrosion, nuclear power plant, machine learning, nondestructive testing, condition-based monitoring.

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“…Non-destructive inspection methods using magnetism is a field that has grown in recent years, such as Magneto Acoustic Emission (MAE) [3] or those that implement Eddy Currents [4][5][6]. One of the magnetic methods that has been in use since the 1960s is the Magnetic Flux Leakage (MFL) [7][8][9][10][11], which induces an external magnetic flux in the material or piece that is being analyzed and whose defects are left behind. exposed when disturbances are found in the said flow.…”

Section: Introductionmentioning

confidence: 99%

Simulations of Magnetic Field Variations Around Rectangular Defects of Ferromagnetic Materials

Oca-Mora

Woo-García²,

Vázquez-Valdes³

et al. 2023

J. Phys.: Conf. Ser.

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The ferromagnetic structures require techniques of defects inspection for their structural monitoring. The early detection of these defects can prevent structural failures that cause economic losses and environment damages. Herein, we present a model for numerical simulations of the magnetic field variations around rectangular defects in ferromagnetic materials by Python programming language. These simulations can predict the behavior of the magnetic field variation caused by rectangular defects considering the study of the magnetic domains of the ferromagnetic material. The numerical simulations of magnetic field around three rectangular defects with different depths (1, 3, and 5 mm) and the same width of 3 mm are studied. The results of the numerical simulations agree well with the behavior of cracks. The proposed model can be used to estimate potential structural failures in ferromagnetic structures.

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Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

Cited by 97 publications

References 123 publications

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Total Unwrapped Phase-Based Diagnosis of Wall Thinning in Nuclear Power Plants Secondary Piping Structures

Simulations of Magnetic Field Variations Around Rectangular Defects of Ferromagnetic Materials

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