Katerina, Lepokva and Chris AldrichAbdulmutaali, Ahmed2025-07-312024-12-20A. Abdulmutaali, Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion of Electrochemical Noise Data and Machine Learning Techniques. Curtin University, 2024.oai:espace.curtin.edu.au:20.500.11937/98063 http://hdl.handle.net/20.500.11937/98063https://hdl.handle.net/20.500.14154/76042The thesis is now available on Curtin University's institutional repository under embargo until 18/06/2027, espace You can access the thesis at https://hdl.handle.net/20.500.11937/98063The 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.206enAnalysisCarbon steelsdeep learning approachDiscrete Wavelet TransformElectrochemical noiseLocalized corrosiononline corrosion monitoringwavelet scalogramswavelet transform analysisWavelet transformsThesis