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https://doi.org/10.17559/TV-20250321002502

Intelligent Deep Learning Based Fault Classification Using Dual-Stream Transformer-CNN with Self-Supervised Feature Refinement for Industrial Applications

K. Ranjani ; Department of Electronics & Communication Engineering, SNS College of Technology, Coimbatore-641035 Tamilnadu, India *
T. Baranidharan ; Department of Electronics & Communication Engineering, KSR College of Technology, Thiruchengode-637215 Tamilnadu, India
J. Ramal ; Saveetha Institute of Medical Technology Sciences, Saveetha University, Chennai, 603203 India

* Dopisni autor.

Sažetak

Fault classification plays a crucial role in industrial engineering, particularly in manufacturing and power generation, where accurate fault detection is essential to prevent system failures, reduce maintenance costs, and enhance operational safety. With the advancement of Industry 4.0 and 5.0, intelligent fault classification techniques leveraging real-time data processing have become increasingly important. This study proposes a deep learning-based fault classification model integrating Dual-Stream Transformer-CNN with Self-Supervised Feature Refinement (DSTC-SSFR) to improve classification accuracy and robustness. The core architecture consists of two parallel processing streams designed to effectively extract both spatial and temporal features from multivariate industrial sensor signals. The spatial stream uses multi-scale 1D Convolutional Neural Networks (1D CNNs) with varying kernel sizes to capture localized fault-related features at different frequency scales. The Bobcat is used for hyperparameter tuning, further enhancing model performance. The proposed approach achieves an overall accuracy of 95.07%, with a precision of 95.10%, recall of 95.07%, and F1-score of 95.07%. Additionally, the model attains a logarithmic loss of 0.1070, a Matthews correlation coefficient (MCC) of 0.9343, and an area under the ROC curve (AUC) of 99.54%. These results demonstrate the model's effectiveness in fault classification, offering a robust and efficient solution for industrial applications in smart manufacturing environments.

Ključne riječi

deep learning; fault classification; industry 4.0; sensor signals; transformer-CNN

Hrčak ID:

342637

URI

https://hrcak.srce.hr/342637

Datum izdavanja:

31.12.2025.

Posjeta: 546 *