Arbitrary crack depth profiling through ACFM data using Type-2 fuzzy logic and PSO algorithm

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Arbitrary crack depth profiling through ACFM data using Type-2 fuzzy logic and PSO algorithm. / Ahmadkhah, Saeed M. ; Hasanzadeh, Reza P. R. ; Papaelias, Mayorkinos.

In: IEEE Transactions on Magnetics, Vol. 55, No. 2, 6200210, 02.2019, p. 1-10.

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@article{3af2a5d3642f4e3787497cd2a26517af,
title = "Arbitrary crack depth profiling through ACFM data using Type-2 fuzzy logic and PSO algorithm",
abstract = "Estimating the shape and depth of cracks presented in metallic structures is one of the main issues of non-destructive testing (NDT) in order to evaluate effectively the structural integrity of a component. The alternating current field measurement (ACFM) technique is one of the most frequently used electromagnetic methods in this regard. Given the experimental nature of NDT methods, fuzzy logic-based methodologies have been widely used for solving the inverse problem. Due to some experimental restrictions, the obtained ACFM signals do not have high certainty. This problem usually leads to a high uncertainty of classical fuzzy rules extracted from low-accuracy ACFM signals. Therefore, applying classical fuzzy membership functions (MFs) exactly with maximum and fixed certainty does not lead to the best crack depth estimation. In this paper, a type-2 fuzzy logic system has been proposed to model the existing uncertainties of ACFM signals with a higher accuracy. Moreover, for regulating the uncertainty parameters of type-2 fuzzy MFs in the proposed model, the particle swarm optimization (PSO) algorithm has been used. Combining PSO with some special features existed in the ACFM signals allows the proposed model to be able to control the certainty of the extracted rules for estimating the exact depth of cracks. Then, the results of the proposed method are compared with the other state-of-the-art techniques for different levels of noise and different crack shapes obtained through simulated and empirical ACFM data. The results show the superiority of the proposed method even in conditions where the training database volume is not adequate.",
keywords = "Alternating current field measurement (ACFM), inverse problem, non-destructive testing (NDT), particle swarm optimization (PSO), type-2 fuzzy logic system (T2FLS)",
author = "Ahmadkhah, {Saeed M.} and Hasanzadeh, {Reza P. R.} and Mayorkinos Papaelias",
year = "2019",
month = feb,
doi = "10.1109/TMAG.2018.2884828",
language = "English",
volume = "55",
pages = "1--10",
journal = "IEEE Transactions on Magnetics",
issn = "0018-9464",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "2",

}

RIS

TY - JOUR

T1 - Arbitrary crack depth profiling through ACFM data using Type-2 fuzzy logic and PSO algorithm

AU - Ahmadkhah, Saeed M.

AU - Hasanzadeh, Reza P. R.

AU - Papaelias, Mayorkinos

PY - 2019/2

Y1 - 2019/2

N2 - Estimating the shape and depth of cracks presented in metallic structures is one of the main issues of non-destructive testing (NDT) in order to evaluate effectively the structural integrity of a component. The alternating current field measurement (ACFM) technique is one of the most frequently used electromagnetic methods in this regard. Given the experimental nature of NDT methods, fuzzy logic-based methodologies have been widely used for solving the inverse problem. Due to some experimental restrictions, the obtained ACFM signals do not have high certainty. This problem usually leads to a high uncertainty of classical fuzzy rules extracted from low-accuracy ACFM signals. Therefore, applying classical fuzzy membership functions (MFs) exactly with maximum and fixed certainty does not lead to the best crack depth estimation. In this paper, a type-2 fuzzy logic system has been proposed to model the existing uncertainties of ACFM signals with a higher accuracy. Moreover, for regulating the uncertainty parameters of type-2 fuzzy MFs in the proposed model, the particle swarm optimization (PSO) algorithm has been used. Combining PSO with some special features existed in the ACFM signals allows the proposed model to be able to control the certainty of the extracted rules for estimating the exact depth of cracks. Then, the results of the proposed method are compared with the other state-of-the-art techniques for different levels of noise and different crack shapes obtained through simulated and empirical ACFM data. The results show the superiority of the proposed method even in conditions where the training database volume is not adequate.

AB - Estimating the shape and depth of cracks presented in metallic structures is one of the main issues of non-destructive testing (NDT) in order to evaluate effectively the structural integrity of a component. The alternating current field measurement (ACFM) technique is one of the most frequently used electromagnetic methods in this regard. Given the experimental nature of NDT methods, fuzzy logic-based methodologies have been widely used for solving the inverse problem. Due to some experimental restrictions, the obtained ACFM signals do not have high certainty. This problem usually leads to a high uncertainty of classical fuzzy rules extracted from low-accuracy ACFM signals. Therefore, applying classical fuzzy membership functions (MFs) exactly with maximum and fixed certainty does not lead to the best crack depth estimation. In this paper, a type-2 fuzzy logic system has been proposed to model the existing uncertainties of ACFM signals with a higher accuracy. Moreover, for regulating the uncertainty parameters of type-2 fuzzy MFs in the proposed model, the particle swarm optimization (PSO) algorithm has been used. Combining PSO with some special features existed in the ACFM signals allows the proposed model to be able to control the certainty of the extracted rules for estimating the exact depth of cracks. Then, the results of the proposed method are compared with the other state-of-the-art techniques for different levels of noise and different crack shapes obtained through simulated and empirical ACFM data. The results show the superiority of the proposed method even in conditions where the training database volume is not adequate.

KW - Alternating current field measurement (ACFM)

KW - inverse problem

KW - non-destructive testing (NDT)

KW - particle swarm optimization (PSO)

KW - type-2 fuzzy logic system (T2FLS)

UR - http://www.scopus.com/inward/record.url?scp=85060527253&partnerID=8YFLogxK

U2 - 10.1109/TMAG.2018.2884828

DO - 10.1109/TMAG.2018.2884828

M3 - Article

VL - 55

SP - 1

EP - 10

JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

SN - 0018-9464

IS - 2

M1 - 6200210

ER -