Abstract
This paper describes a non-intrusive method for collecting data about internal corrosion damages in AISI-304 stainless steel plates and classifying them according to severity. The mapping of the electric potential gradient is derived using the potential drop technique, which is then analyzed using image processing techniques including edge enhancement and segmentation. Simulations were run using finite element modeling to produce examples of damaged plates, with four types of defects that can be considered part of pitting corrosion. The image processing stage plays the role of an extractor of features that, when employed as inputs of machine learning algorithms, make it possible to determine the damage severity. With the Gradient Boosting regressor, the maximum absolute error of 0.879 mm was obtained in the estimate of the depth of the defects. Additionally, with the application of a Convolutional Neural Network, an accuracy of 94.84% was achieved to classify of the severity of the damages.
Similar content being viewed by others
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, GS., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng, X. (2015). TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/, software available from tensorflow.org
Al-Shamari, A., Al-Sulaiman, S., Al-Mithin, A., Jarragh, A., Prakash, S.S., & Islam, M. (2012). Internal corrosion monitoring and management strategy for Kuwait’s pipeline network system. In: NACE corrosion conference, NACE, Salt Lake City
Amaral, J., Dos Santos Pinto, G. L., Vieira Pinheiro, G. R., Gomes Silva, V., & Ponciano Gomes, J. A. (2020). A non-intrusive system to classify the severity of damages caused by internal corrosion using the potential drop technique and electrical image mapping. Journal of Integrated Circuits and Systems, 15(3), 1–7. https://doi.org/10.29292/jics.v15i3.181.
Angayarkanni, N., & Durairaj, K. (2015). Euclidean distance transform (edt) algorithm applied to binary image for finding breast cancer. Biomedical and Pharmacology Journal, 8, 407–411.
ASTM (2018). Standard guide for examination and evaluation of pitting corrosion. ASTM, G46-94 (Reapproved 2018)
Atha, D., & Jahanshahi, M. (2017). Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Structural Health Monitoring: An International Journal p 147592171773705, https://doi.org/10.1177/1475921717737051
Awad, M., & Khanna, R. (2015). Support vector regression, (pp. 67–80) Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4
Bradski, G. (2000). The opencv library. Dr Dobb’s Journal of Software Tools, 25, 122–125.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), https://doi.org/10.1023/A:1010933404324
Brinnel, V., Döbereiner, B., & Münstermann, S. (2014). Characterizing ductile damage and failure: application of the direct current potential drop method to uncracked tensile specimens. Procedia Materials Science, 3, 1161–1166. https://doi.org/10.1016/j.mspro.2014.06.189.
Chen, T., & Guestrin, C., (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’16, pp. 785–794,https://doi.org/10.1145/2939672.2939785
Chollet, F. (2015). Keras. https://keras.io
COMSOL (2018) Comsol ac\(/\)dc module user’s guide. www.comsol.com, COMSOL Multiphysics, COMSOL AB
Corcoran, J., Davies, C. M., Cawley, P., & Nagy, P. B. (2020). A quasi-dc potential drop measurement system for material testing. IEEE Transactions on Instrumentation and Measurement, 69(4), 1313–1326. https://doi.org/10.1109/TIM.2019.2908509.
Demirović, D. (2019). An implementation of the mean shift algorithm. Image Processing On Line, 9, 251–268. https://doi.org/10.5201/ipol.2019.255.
Doremus, L., Nadot, Y., Henaff, G., Mary, C., & Pierret, S. (2015). Calibration of the potential drop method for monitoring small crack growth from surface anomalies - crack front marking technique and finite element simulations. International Journal of Fatigue, 70, 178–185. https://doi.org/10.1016/j.ijfatigue.2014.09.003.
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). Wiley-Interscience.
Feydo, M., Pellegrino, B., & Strachan, S., (2017) Non-intrusive ultrasonic corrosion-rate measurement in lieu of manual and intrusive methods. In: NACE corrosion conference, NACE, Boalsburg
Friedman, J. (2000). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29, https://doi.org/10.1214/aos/1013203451
Gan, F., Tian, G., Wan, Z., Liao, J., & Li, W. (2016). Investigation of pitting corrosion monitoring using field signature method. Journal of the International Measurement Confederation, 82, 46–54. https://doi.org/10.1016/j.measurement.2015.12.040.
Hayt, W. H. J., & Buck, J. A. (2013). Eletromagnetismo (8th ed.). AMGH.
He, K., & Sun, J. (2014). Convolutional neural networks at constrained time cost. arXiv:1412.1710 [cs]
Hoang, ND., & Duc, T. (2019). Image processing based detection of pipe corrosion using texture analysis and metaheuristic-optimized machine learning approach. Computational Intelligence and Neuroscience In Press:13, doi: https://doi.org/10.1155/2019/8097213
Ioffe, S., & Szegedy, C. (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv: 1502.03167
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: with applications, R. Springer Publishing Company, Incorporated, http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf
Khan, A., Sohail, A., Zahoora, U., & Qureshi, AS. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, https://doi.org/10.1007/s10462-020-09825-6, arXiv:1901.06032
Krizhevsky, A., Sutskever, I., & Hinton, GE. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25 (NIPS 2012) (pp. 1097–1105), doi: https://doi.org/10.1145/3065386
Lantz, B. (2015). Machine Learning with R, (2nd ed.) Packt Publishing, Birmingham B3 2PB, UK
Moreira, A.R., de Almeida, N.L., Chaves, L.F.F., Silva, V.G., Vaz, G.L., (2019) Non-intrusive systems for monitoring internal corrosion of carbon steel pipes. In: NACE corrosion conference, NACE, Nashville, 13358
NACE International (1999). Standard recommended practice : preparation, installation, analysis, and interpretation of corrosion coupons in oilfield operations. Technical report, NACE.
NACE International Task Group (TG) 390. (2012). Techniques for Monitoring Corrosion and Related Parameters in Field Applications. Techical report, NACE.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Édouard Duchesnay (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(85):2825–2830, http://jmlr.org/papers/v12/pedregosa11a.html
Pinheiro, G.R.V., Silva, VG., da Cunha Ponciano Gomes, JA., do Amaral, JLM., & dos Santos Pinto, GL. (2019). Obtenção da imagem de defeitos em materiais por mapeamento elétrico. In Anais do 14\(^\circ \)Simpósio Brasileiro de Automação Inteligente, Galoa, https://doi.org/10.17648/sbai-2019-111545
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. arXiv: 1505.04597
Ryu, K., Lee, T., cheon Baek, D., & won Park, J. (2020). Pipe thinning model development for direct current potential drop data with machine learning approach. Nuclear Engineering and Technology, 52(4), 784–790. https://doi.org/10.1016/j.net.2019.10.004.
Seghier, M. E. A. B., Keshtegar, B., Tee, K. F., Zayed, T., Abbassi, R., & Trung, N. T. (2020). Prediction of maximum pitting corrosion depth in oil and gas pipelines. Engineering Failure Analysis, 112, 104505. https://doi.org/10.1016/j.engfailanal.2020.104505.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556v6
Soille, P. (2003). Morphological image analysis: principles and applications (2nd ed.). Springer-Verlag.
Sposito, G. (2009). Advances in potential drop techniques for non-destructive testing. Phd thesis, Imperial College London, Londres
Sposito, G., Cawley, P., & Nagy, P. (2010). Potential drop mapping for the monitoring of corrosion or erosion. JNDT&E International, pp. 394–402, https://doi.org/10.1016/j.ndteint.2010.03.005
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56), 1929–1958.
Stanford. (2019). Cs231n convolutional neural networks for visual recognition. https://cs231n.github.io/convolutional-networks/, stanford Vision and Learning Lab - Stanford University
Szeliski, R. (2011). Computer vision. Texts in Computer Science, Springer, London, London,. https://doi.org/10.1007/978-1-84882-935-0.
Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, I., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., van Mulbregt, P., & SciPy 10 Contributors (2020). SciPy 1.0: fundamental algorithms for scientific computing in python. Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2
Vriesman, D., Britto Junior., A., Zimmer, A., & Lameiras Koerich, A. (2019). Texture cnn for thermoelectric metal pipe image classification. In 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI), pp. 569–574, https://doi.org/10.1109/ICTAI.2019.00085
Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). scikit-image: image processing in python. PeerJ, 2, 453. https://doi.org/10.7717/peerj.453.
Zeiler, M.D., & Fergus, R. (2013). Visualizing and understanding convolutional networks. arXiv:1311.2901
Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms (1st ed.). Chapman & Hall/CRC.
Acknowledgements
An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020). The authors would like to acknowledge the financial support provided by FAPERJ —Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro and CAPES—Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pinto, G., Amaral, J., Pinheiro, G.R.V. et al. Non-intrusive Internal Corrosion Characterization using the Potential Drop Technique for Electrical Mapping and Machine Learning. J Control Autom Electr Syst 33, 183–197 (2022). https://doi.org/10.1007/s40313-021-00823-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40313-021-00823-9