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    Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods

    Hou Y 2018.pdf (7.567Mb)
    Access Status
    Open access
    Authors
    Hou, Yang
    Date
    2018
    Supervisor
    Prof. Chris Aldrich
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    WA School of Mines: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/73525
    Collection
    • Curtin Theses
    Abstract

    Although electrochemical noise (EN) has been studied for decades, the optimal approach for the analysis of EN data remains uncertain. This research innovatively combined the use of recurrence quantification analysis of electrochemical noise data and machine learning methods to develop models for corrosion monitoring and corrosion type identification. Case studies demonstrate that the proposed methodologies are potentially feasible for the development of online corrosion monitoring programs.

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