Multilayer perceptron neural network and eddy current technique for estimation of the crack depth on massive metal structures
383 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.77.2022.3-12Keywords:
Non-destructive evaluation; Eddy-current technique; Feature extraction; Multi-frequency approach; Multilayer perceptron (MLP) neural network.Abstract
This paper introduces a method for estimating the maximum depth (sub-millimeter) of minor cracks on the surface of aluminum plates used in the aeronautical industry. A set of C-scan eddy current (EC) images, including real and imaginary parts of the impedance, is analyzed to extract suitable features after reducing noise effects, such as background noises and edge noises. Based on the obtained features, e.g. maximum impedance, the background feature, background noises, type of sensors, a Multilayer Perceptron (MLP) Neural Network is built to estimate the maximum depth of the cracks. The network is optimized based on loss functions, such as mean absolute error and mean squared error. An optimal network structure with five neurons in the first hidden layer and eight neurons in the second hidden layer is chosen. The obtained result indicated that the relative error of estimations is lower than 10% for almost all experimental tested samples.
References
[1]. N. Yusa, H. Huang, and K. Miya, “Numerical evaluation of the ill-posedness of eddy current problems to size real cracks,” NDT & E International, Vol. 40, no. 3, pp. 185–191, (2007).
[2]. M. Zergoug, S. Lebailia, and G. Kamel, “Characterization of the corrosion by eddy current,” p. 7.
[3]. D. C. Copley, “Eddy-Current Imaging for Defect Characterization,” in Review of Progress in Quantitative Nondestructive Evaluation, D. O. Thompson and D. E. Chimenti, Eds. Boston, MA: Springer US, pp. 1527–1540 (1983).
[4]. L. Xie, B. Gao, G. Y. Tian, J. Tan, B. Feng, and Y. Yin, “Coupling pulse eddy current sensor for deeper defects NDT,” Sensors and Actuators A: Physical, Vol. 293, pp. 189–199, (2019).
[5]. D. Kim, L. Udpa, and S. Udpa, “Remote field eddy current testing for detection of stress corrosion cracks in gas transmission pipelines,” Materials Letters, Vol. 58, no. 15, pp. 2102–2104, (2004).
[6]. S. Xie, Z. Duan, J. Li, Z. Tong, M. Tian, and Z. Chen, “A novel magnetic force transmission eddy current array probe and its application for nondestructive testing of defects in pipeline structures,” Sensors and Actuators A: Physical, Vol. 309, p. 112030, (2020).
[7]. Z. Chu, Z. Jiang, Z. Mao, Y. Shen, J. Gao, and S. Dong, “Low-power eddy current detection with 1-1 type magnetoelectric sensor for pipeline cracks monitoring,” Sensors and Actuators A: Physical, Vol. 318, p. 112496, (2021).
[8]. Y. He et al., “Pulsed eddy current technique for defect detection in aircraft riveted structures,” NDT & E International, Vol. 43, no. 2, pp. 176–181, (2010).
[9]. J. H. Espina-Hernández, E. Ramírez-Pacheco, F. Caleyo, J. A. Pérez-Benitez, and J. M. Hallen, “Rapid estimation of artificial near-side crack dimensions in aluminium using a GMR-based eddy current sensor,” NDT & E International, Vol. 51, pp. 94–100, (2012).
[10]. Y. Le Diraison, P.-Y. Joubert, and D. Placko, “Characterization of subsurface defects in aeronautical riveted lap-joints using multi-frequency eddy current imaging,” NDT & E International, Vol. 42, no. 2, pp. 133–140, (2009).
[11]. D. J. Pasadas, A. L. Ribeiro, T. J. Rocha, and H. G. Ramos, “Open crack depth evaluation using eddy current methods and GMR detection,” in 2014 IEEE Metrology for Aerospace (MetroAeroSpace), pp. 117–121, (2014).
[12]. R. Menezes, A. L. Ribeiro, and H. G. Ramos, “Evaluation of crack depth using eddy current techniques with GMR-based probes,” in 2015 IEEE Metrology for Aerospace (MetroAeroSpace), Benevento, Italy, pp. 335–33, (2015).
[13]. K. Kwon and D. M. Frangopol, “Bridge fatigue assessment and management using reliability-based crack growth and probability of detection models,” Probabilistic Engineering Mechanics, Vol. 26, no. 3, pp. 471–480, (2011).
[14]. D. G. Park, C. S. Angani, and Y. M. Cheong, “Differential Pulsed eddy current probe to detect the sub surface Cracks in a Stainless Steel Pipe,” p. 6.
[15]. K. Demachi, T. Hori, and S. Perrin, “Crack depth estimation of non-magnetic material by convolutional neural network analysis of eddy current testing signal,” Journal of Nuclear Science and Technology, Vol. 57, no. 4, pp. 401–407, (2020).
[16]. E. Mohseni, D. R. França, M. Viens, W. F. Xie, and B. Xu, “Finite Element Modelling of a Reflection Differential Split-D Eddy Current Probe Scanning Surface Notches,” J Nondestruct Eval, Vol. 39, no. 2, p. 29, (2020).
[17]. M. Jesenik and M. Trlep, “Finding a Crack and Determining Depth in a Material,” Przegląd Elektrotechniczny, Vol. 89, no. 2b, pp. 64–67, (2013).
[18]. Z. Wang and Y. Yu, “Traditional Eddy Current–Pulsed Eddy Current Fusion Diagnostic Technique for Multiple Micro-Cracks in Metals,” Sensors (Basel), Vol. 18, no. 9, (2018).
[19]. L. Tian, C. Yuhua, Y. Chun, H. Xuegang, Z. Bo, and B. Libing, “Data-Driven Method for the Measurement of Thickness/Depth Using Pulsed Eddy Current,” Sensors and Materials, p. 1325, (2017).
[20]. F. Nafiah, A. Sophian, M. R. Khan, S. B. Abdul Hamid, and I. M. Zainal Abidin, “Image-Based Feature Extraction Technique for Inclined Crack Quantification Using Pulsed Eddy Current,” Chin. J. Mech. Eng., Vol. 32, no. 1, p. 26, (2019).
[21]. M. Smetana, L. Behun, D. Gombarska, and L. Janousek, “New Proposal for Inverse Algorithm Enhancing Noise Robust Eddy-Current Non-Destructive Evaluation,” Sensors (Basel), Vol. 20, no. 19, (2020).
[22]. L. T. Cung, T. D. Dao, P. C. Nguyen, and T. D. Bui, “A model-based approach for estimation of the crack depth on a massive metal structure,” Measurement and Control, Vol. 51, no. 5–6, pp. 182–191, (2018).
[23]. S. Jiao, J. Li, F. Du, L. Sun, and Z. Zeng, “Characteristics of Eddy Current Distribution in Carbon Fiber Reinforced Polymer,” Journal of Sensors, Vol. 2016, p. e4292134, (2016).
[24]. B. H. Shekar and G. Dagnew, “Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data,” in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp. 1–8, (2019).
[25]. J. Brownlee, “Use Early Stopping to Halt the Training of Neural Networks At the Right Time,” Machine Learning Mastery, (2018).
