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    Analysis of electrochemical noise data by use of recurrence quantification analysis and machine learning methods

    Access Status
    Fulltext not available
    Authors
    Hou, Y.
    Aldrich, Chris
    Lepkova, Katerina
    Machuca Suarez, Laura
    Kinsella, Brian
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Hou, Y. and Aldrich, C. and Lepkova, K. and Machuca Suarez, L. and Kinsella, B. 2017. Analysis of electrochemical noise data by use of recurrence quantification analysis and machine learning methods. Electrochimica Acta. 256: pp. 337-347.
    Source Title
    Electrochimica Acta
    DOI
    10.1016/j.electacta.2017.09.169
    ISSN
    0013-4686
    School
    Dept of Mining Eng & Metallurgical Eng
    URI
    http://hdl.handle.net/20.500.11937/57708
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 By use of recurrence quantification analysis (RQA), twelve features were extracted from the electrochemical noise signals generated by three types of corrosion: uniform, pitting and passivation. Machine learning methods, i.e. linear discriminant analysis (LDA) and random forests (RF), were used to identify the different corrosion types from those features. Both models gave satisfactory performance, but the RF model showed better prediction accuracy of 93% than the LDA model (88%). Furthermore, an estimation of the importance of the variables by use of the RF model suggested the RQA variables laminarity (LAM) and determinism (DET) played the most significant role with regard to identification of corrosion types. In addition, the comparison of noise resistance with the resistance obtained from EIS measurement showed that the noise resistance can be used for monitoring corrosion rate variations not only for uniform corrosion and passivation, but also for pitting.

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