Characterization of defects in Magnetic Flux Leakage (MFL) images using wavelet transform and neural network

Daniel, Jackson; Mohanagayathriand, R.; Abudhahir, A.

doi:10.1109/ecs.2014.6892788

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…It also the most suitable method in solving the issues of magnetic field effect around the transmission line caused by circular cross-section of voltage conductors. There are several software that can characterizes defect by using raw data, including ANSYS, MagNet, JSOL, COMSOL, Multiphysics, OPERA, MAXWELL, FLUX and others [87][88][89]. These software has their own ability in computing electrostatics and magnetostatics elements.…”

Section: Fem Background In Mflmentioning

confidence: 99%

Theory and development of magnetic flux leakage sensor for flaws detection: A review

Sharif¹,

Ramli²,

Mohamed³

et al. 2019

IJAAS

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<p>This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece. </p>

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Section: Fem Background In Mflmentioning

confidence: 99%

Theory and development of magnetic flux leakage sensor for flaws detection: A review

Sharif¹,

Ramli²,

Mohamed³

et al. 2019

IJAAS

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“…By means of time-frequency analysis, median and adaptive filtering, as well as interpolation, Mao Bingyi preprocessed MFL detection signals [4]. Daniel J adopted a variety of different wavelet-based denoising techniques to remove noise from the raw data [5]. However, the limitation is that there is no effective solution to filtering out the noise overlapping with the power spectrum of the defect.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Wang

Jia

et al. 2021

IEEE Access

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Magnetic flux leakage (MFL) detection technology provides an effective method to conduct high-speed detection of the damage suffered by rail surface. With regard to high-speed detection, there is frequently a complex noise contained in the magnetic signal of railway leakage, which is similar to the amplitude of defect and the overlaps of frequency spectrum. In this paper, an improved adaptive filtering method is proposed to solve the problem caused by filtering the MFL signal on the rail top surface. Through the characteristics of distribution shown by defects on the top surface of the railway and those of the data collected by the rail top array sensor, this method is applied to construct a virtual channel containing almost only interference signals but no defects. Then, in combination with the adaptive filtering algorithm, the virtual channel signal is taken as the reference input of the adaptive canceller, each single channel MFL signal is taken as the original input of the adaptive canceller, and the filtered MFL signal is taken as the output. Then, the MFL signal of rail top is collected by the train at the speed of 30km / h on the manual calibration line. According to the experimental results, the noise intensity of MFL signal is reduced by up to 81.44%. In addition, the filtering method is adopted to process MFL signals with different directions and varying detection speed. As indicated by the results, the noise intensity of MFL signal is reduced by more than 74%. INDEX TERMS High speed rail detection; Magnetic flux leakage signal; Adaptive filtering algorithm; Reference signal; Adaptive noise canceller. KAILUN JI received the B.Eng. and the M.S. degree in techniques and instruments of measurement from the Nanjing University of Aeronautics and Astronautics, China, in 2016 and 2020. He is currently pursuing the Ph.D. degree at the Nanjing University of Aeronautics and Astronautics of China. His research interests include nondestructive testing, signal processing and system modeling.

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“…Dalgacık dönüşüm yöntemleri ise farklı dalgacık fonksiyonları kullanarak, işaretin frekans düzlemindeki değişiminin, zaman domeni ile ilişkilendirilmesini sağlar. Bu yüzden dalgacık dönüşüm yöntemleri, bir işaretin analizi ve filtrelenmesi gibi işlemlerde yaygın olarak kullanılmaktadır [9,10].…”

Section: Introductionunclassified

Manyetik Kaçak Akı İşaret Gürültüsü İçin Dalgacık Dönüşümü ve Tekrarlı Gauss Filtreleme Yöntemlerinin İncelenmesi

Kara

Çelik

2021

International Journal of Advances in Engineering and Pure Sciences

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ÖzManyetik kaçak akı (MKA) yöntemi, ferromanyetik özellikteki materyallerde oluşan çatlak, delik, korozyon gibi kusurları algılama amaçlı kullanılan tahribatsız test yöntemlerinden biridir. MKA sensörlerinden alınan işaretler test edilen materyal yapısında oluşan kusurları tanımlamakla birlikte gürültü bileşenlerini de içermektedir. Gürültü miktarı ve niteliği ise test sistemi ve materyal yapısına göre farklılıklar göstermektedir. Uygun olmayan bir filtreleme işlemi sonucunda, kusurları temsil eden işaretler, gürültü içerisinde kaybolmakta veya bozularak anlamını kaybetmektedir. Bu çalışmada MKA yöntemi ile alınan işaretler üzerinde dalgacık dönüşümü ve tekrarlı Gauss filtreleme yöntemlerinin etkinliği incelenmektedir. MKA işaretlerini üretmek amacıyla, ANSYS Maxwell benzetim ortamında manyetik test sistemi tasarlanmıştır. Test materyali olarak, üzerinde farklı büyüklükte 3 yapay kusur bulunan bir çelik plaka kullanılmıştır. Manyetik sistemden alınan işaretler üzerinde, ayrık dalgacık dönüşümü ve tekrarlı Gauss fitreleri uygulanmıştır. Filtrelenen işaretler üzerinde oluşan işaret bozulmaları ve simetri bozukluğu ele alınarak incelenmiştir. Deney sonuçlarına göre, MKA işaretleri üzerinde, tekrarlı Gauss filtresinin dalgacık dönüşümü yöntemlerinden daha başarılı sonuçlar ürettiği görülmektedir.

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Characterization of defects in Magnetic Flux Leakage (MFL) images using wavelet transform and neural network

Cited by 12 publications

References 2 publications

Theory and development of magnetic flux leakage sensor for flaws detection: A review

Theory and development of magnetic flux leakage sensor for flaws detection: A review

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Manyetik Kaçak Akı İşaret Gürültüsü İçin Dalgacık Dönüşümü ve Tekrarlı Gauss Filtreleme Yöntemlerinin İncelenmesi

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