Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods
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 cor...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/73525 |
| _version_ | 1848763036032040960 |
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| author | Hou, Yang |
| author_facet | Hou, Yang |
| author_sort | Hou, Yang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:57:04Z |
| format | Thesis |
| id | curtin-20.500.11937-73525 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:57:04Z |
| publishDate | 2018 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-735252018-12-14T05:55:03Z Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods Hou, Yang 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. 2018 Thesis http://hdl.handle.net/20.500.11937/73525 Curtin University fulltext |
| spellingShingle | Hou, Yang Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title | Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title_full | Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title_fullStr | Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title_full_unstemmed | Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title_short | Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods |
| title_sort | corrosion monitoring based on recurrence quantification analysis of electrochemical noise and machine learning methods |
| url | http://hdl.handle.net/20.500.11937/73525 |