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dc.contributor.authorHou, Yang
dc.contributor.supervisorProf. Chris Aldrichen_US

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.

dc.publisherCurtin Universityen_US
dc.titleCorrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methodsen_US
curtin.departmentWA School of Mines: Minerals, Energy and Chemical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US

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