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Multi-q pattern classification of polarization curves

Author

Listed:
  • Fabbri, Ricardo
  • Bastos, Ivan N.
  • Neto, Francisco D. Moura
  • Lopes, Francisco J.P.
  • Gonçalves, Wesley N.
  • Bruno, Odemir M.

Abstract

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves.

Suggested Citation

  • Fabbri, Ricardo & Bastos, Ivan N. & Neto, Francisco D. Moura & Lopes, Francisco J.P. & Gonçalves, Wesley N. & Bruno, Odemir M., 2014. "Multi-q pattern classification of polarization curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 332-339.
    • Handle: RePEc:eee:phsmap:v:395:y:2014:i:c:p:332-339
    DOI: 10.1016/j.physa.2013.09.048
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    References listed on IDEAS

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    1. Fabbri, Ricardo & Gonçalves, Wesley N. & Lopes, Francisco J.P. & Bruno, Odemir M., 2012. "Multi-q pattern analysis: A case study in image classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4487-4496.
    2. Balázs, L. & Gouyet, J.-F., 1995. "Two-dimensional pitting corrosion of aluminium thin layers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 217(3), pages 319-338.
    3. Hang Zhang & Susan Albin, 2009. "Detecting outliers in complex profiles using a χ control chart method," IISE Transactions, Taylor & Francis Journals, vol. 41(4), pages 335-345.
    4. De Magalhães, M.S. & Costa, A.F.B. & Moura Neto, F.D., 2009. "A hierarchy of adaptive control charts," International Journal of Production Economics, Elsevier, vol. 119(2), pages 271-283, June.
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    Cited by:

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