Designing and Investigating a Nondestructive Magnetic Flux Leakage Detection System for Quantitatively Identifying Wire Defects

Ding, Dawei; Lu, Jialei; Xu, Fengyu

doi:10.1109/jsen.2022.3206325

Cited by 8 publications

(3 citation statements)

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“…In addition, the RF algorithm can manage large datasets efficiently, which, on the other hand, increases the number of calculations and the memory overhead. For instance, Dawei et al [58] propose a non-destructive magnetic flux leakage detection system to precisely identify damage in cable wires. Modeling and simulation approaches were used to design the system.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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“…With the development of MFL testing technology, a growing concern is the accuracy and effectiveness of defect size estimation, and hence the quantitative method has become the mainstream of research. The relationship between the leakage magnetic field distribution and the defect characteristics has been studied extensively using laboratory experiments [12,13], analytic analysis with the magnetic dipole model (MDM) [14,15] and the finite-element model [16,17]. This enables one to extract defect features based on statistical methods and signal processing techniques, which results in a well-established defect feature library [18].…”

Section: Introductionmentioning

confidence: 99%

Magnetic flux leakage defect size estimation method based on physics-informed neural network

Xiong,

Liu,

Hou

et al. 2023

Phil. Trans. R. Soc. A.

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Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue ‘Physics-informed machine learning and its structural integrity applications (Part 2)’.

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“…Refs. [ 19 , 20 ] studied the influence of magnet size, magnetic pole spacing, and lift-off distance on the magnetic leakage signal by the element simulation method. A simple magnetic leakage detection device was designed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Defect Detection Method for Overhead Ground Wire

Xiao,

Xiong,

Zhang

et al. 2023

Sensors

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Overhead ground wires typically have strong axial tension and are prone to structural defects caused by corrosion and lightning strikes, which could lead to serious safety hazards. Therefore, it is important to detect defects accurately and quickly to avoid those problems. Existing defect detection methods for overhead ground wires are mainly traditional metal defect detection methods, including eddy current detection, ultrasonic detection, and manual visual inspection. However, those methods have problems of low detection efficiency, high environmental requirements, and insufficient reliability. To solve the above problems, this paper studies a novel type of defect detection technology for overhead ground wire. Firstly, the magnetic leakage characteristics around the defects of overhead ground wires are analyzed, and the defect detection device is designed. Then, the influence of air gap, lift-off distance, defect width, and cross-sectional loss rate on the magnetic flux leakage signal is studied, a novel defect detection method for overhead ground wire is proposed, and experimental verification is carried out. The results show that the proposed method can accurately locate and quantify the defect, which has the advantages of good reliability and high efficiency and lays the foundation for preventing accidents caused by defective overhead ground wires.

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Designing and Investigating a Nondestructive Magnetic Flux Leakage Detection System for Quantitatively Identifying Wire Defects

Cited by 8 publications

References 0 publications

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Magnetic flux leakage defect size estimation method based on physics-informed neural network

A Novel Defect Detection Method for Overhead Ground Wire

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