Research on Method of Identifying CO2 Corrosion Type Based on Morphology Image and Support Vector Machine

Tong Gao; Quan Liu; Yi Han Gao; Lei Yue

doi:10.4028/www.scientific.net/AMM.556-562.2953

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Research on Method of Identifying CO2 Corrosion...

Research on Method of Identifying CO₂ Corrosion Type Based on Morphology Image and Support Vector Machine

Abstract:

Aiming to solve the difficulty of recognize corrosion types for complex CO₂ corrosion process with data, this paper presents a recognition method of CO₂ corrosion types using SVM as recognizer. Surface morphology images of N8 steel corroded by CO₂ are decomposed into sub-images by wavelet after gray processing and gray enhancement, and energy information of sub-images is extracted as eigenvector. SVM classifier is constructed based on the three sample sets of no corrosion, pitting corrosion and uniform corrosion, which can recognize CO₂ corrosion types accurately. Superiority of this method is verified by comparison with recognition result of neural network.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

2953-2956

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.2953

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Online since:

May 2014

Authors:

Tong Gao*, Quan Liu, Yi Han Gao, Lei Yue

Keywords:

CO₂ Corrosion, Gray Processing, Morphology Image, SVM, Wavelet Decomposition

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References

[1] Lili Sun, Yi Su, Rui Jia: Prediction of CO2 Corrosion in N80 Steel Based on BP Artificial Neural Networks Methods, Ordnance Material Science and Engineering, Vol. 35(2012), pp.14-16.

Google Scholar

[2] Haiyang Zhao, Minqiang Xu, Jindong Wang: An Improved Binary Tree SVM and Application for Fault Diagnosis, Journal of Vibration Engineering, Vol. 26(2013), pp.764-770.

Google Scholar

[3] Chiang LH, Kotanchek M E, Kordon A K. Faul Diagnosis Based on Fisher Discriminant Analysis and Support Vector Machines[J]. Computers& Chemical Engineering. 2004, 28 (8): 1389-1401.

DOI: 10.1016/j.compchemeng.2003.10.002

Google Scholar

[4] Wei Zhang: Characteristics Extraction of Metallic Corrosion MorPhology for Corrosion Diagnosis [D]. Dalian: Dalian University of Technology, (2004).

Google Scholar

[5] Livens,S., Scheunders,P., Wouwer G.V., Dyck D.V., Smets.T., Winkelmans, J. &Bogaerts, W. A texture analysis approach to corrosion image classification, Microscopy, Microanalysis, Microstruchture, Vol. 7(1996), p. l-10.

DOI: 10.1051/mmm:1996110

Google Scholar