Corrosion type identification in flanged joints using recurrent neural networks on electrochemical noise measurements
Source abstract
Abstract
Abstract Bolted flanged joints are essential for connecting piping and process equipment but are vulnerable to localized corrosion that leads to sudden, unpredictable leaks. Electrochemical noise (EN) measurements can detect such corrosion, yet processing EN data is time-consuming and requires expertise. This study applies recurrent neural networks (RNNs) to automate corrosion type identification on flange surfaces using raw EN signals from spontaneous electrochemical reactions. In this work, supervised, hybrid, and unsupervised ML approaches are evaluated using experimentally obtained EN data. Among supervised models, the long short-term memory (LSTM) model achieves 93.62% accuracy. A hybrid method combining LSTM autoencoder features with a random forest classifier improves accuracy to 97.85%. An unsupervised method using LSTM autoencoder, principal component analysis, and k-means clustering also shows strong potential for real-time corrosion monitoring. Automated identification of corrosion types on flanged joints supports more effective material protection strategies, reducing the risk of failure in critical infrastructure.
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Research context
OpenAlex topics: Structural Integrity and Reliability Analysis · Hydrogen embrittlement and corrosion behaviors in metals · Material Properties and Failure Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
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