Deep Learning for Corrosion Monitoring Virtual Sensor and Predictive Modelling Approaches in Industrial Water Pipeline

Abstract

Industrial water pipeline corrosion constitutes major risks in maintenance expenses, protection & performance. Physical indicators & periodic examinations were the core of conventional tracking approaches which might be expensive as well as ineffective. The present study examines corrosive monitoring in corporate pipelines through predictive modelling techniques & artificial detectors powered by deep learning. Machine learning algorithms have the potential of accurately forecasting the rates of corrosion along with spotting irregularities by employing real-time information using existing detectors comprising pressure, temperature & fluid flow. Different kinds of designs are investigated regarding their capacity to detect rusting trends which consists of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The proposed approach makes it possible to perform in progress, safe monitoring that minimizes maintenance expenses & increases pipeline durability. The outcomes compared to studies highlight how machine learning systems may enhance earlier detection of defects & erosion prediction, leading to higher robustness and efficient pipeline architecture.

Keywords
Industrial water pipeline corrosion, Recurrent Neural Networks, Convolutional Neural Networks, Machine Learning, Predictive modelling techniques and artificial detectors.