A Comparative Study of Machine Learning Techniques for AE-Based Corrosion Detection with Emphasis on Transformer Models

Corrosion-induced damage poses a critical threat to the structural integrity of fluid transport pipelines, necessitating advanced detection strategies for early intervention. This study investigates the use of acoustic emission (AE) monitoring in conjunction with machine learning techniques to identify anomalies indicative of corrosion. A comprehensive analysis of supervised, unsupervised, semi-supervised, and self-supervised learning methods is presented, with emphasis on their suitability for AE-based anomaly detection. Building upon this foundation, we implement and evaluate multiple machine learning models—including K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—and compare them to a Transformer-based model integrated into a hybrid CNN-Transformer architecture. Experimental results demonstrate that the hybrid model outperforms all baselines, achieving R-squared values of 0.7037 for Acoustic Signal Level (ASL) and 0.6836 for Root Mean Square (RMS), thus confirming its superior ability to capture both local and long-range dependencies in acoustic emission data. A systematic review of recent Transformer-based corrosion detection models further contextualizes the results. This research highlights the promise of Transformer-based models in robust, real-time corrosion monitoring and offers a pathway toward more intelligent, machine learning-driven infrastructure maintenance systems.

@article{Ali2025,
  title     = {A Comparative Study of Machine Learning Techniques for AE-Based Corrosion Detection with Emphasis on Transformer Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Osama Shahid Ali and Lukman B A Rahim},
  doi       = {10.14569/IJACSA.2025.0160765},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160765}
}