Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion of Electrochemical Noise Data and Machine Learning Techniques
The study addresses effectively monitoring and controlling the corrosion process using electrochemical noise analysis in different scenarios. It explores the challenges in feature extraction and analytical methods. It also proposes novel systematic approaches to overcome these challenges using deep...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/98063 |
| _version_ | 1848766355414712320 |
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| author | Abdulmutaali, Ahmed |
| author_facet | Abdulmutaali, Ahmed |
| author_sort | Abdulmutaali, Ahmed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The study addresses effectively monitoring and controlling the corrosion process using electrochemical noise analysis in different scenarios. It explores the challenges in feature extraction and analytical methods. It also proposes novel systematic approaches to overcome these challenges using deep learning models such as stochastic neighbour embedding (t-SNE) and principal component analysis (PCA). This work provides a potential quantification analysis method for online corrosion monitoring and control, widely considered the industry standard. |
| first_indexed | 2025-11-14T11:49:50Z |
| format | Thesis |
| id | curtin-20.500.11937-98063 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:50Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-980632025-07-11T00:46:33Z Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion of Electrochemical Noise Data and Machine Learning Techniques Abdulmutaali, Ahmed The study addresses effectively monitoring and controlling the corrosion process using electrochemical noise analysis in different scenarios. It explores the challenges in feature extraction and analytical methods. It also proposes novel systematic approaches to overcome these challenges using deep learning models such as stochastic neighbour embedding (t-SNE) and principal component analysis (PCA). This work provides a potential quantification analysis method for online corrosion monitoring and control, widely considered the industry standard. 2024 Thesis http://hdl.handle.net/20.500.11937/98063 Curtin University restricted |
| spellingShingle | Abdulmutaali, Ahmed Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion of Electrochemical Noise Data and Machine Learning Techniques |
| title | Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion
of Electrochemical Noise Data and Machine Learning Techniques |
| title_full | Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion
of Electrochemical Noise Data and Machine Learning Techniques |
| title_fullStr | Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion
of Electrochemical Noise Data and Machine Learning Techniques |
| title_full_unstemmed | Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion
of Electrochemical Noise Data and Machine Learning Techniques |
| title_short | Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion
of Electrochemical Noise Data and Machine Learning Techniques |
| title_sort | developing real-time corrosion monitoring: a cutting-edge fusion
of electrochemical noise data and machine learning techniques |
| url | http://hdl.handle.net/20.500.11937/98063 |