Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov–Arnold Networks DOI Creative Commons
Spyros Rigas, Michalis Papachristou,

Ioannis Nektarios Sotiropoulos

et al.

Entropy, Journal Year: 2025, Volume and Issue: 27(4), P. 403 - 403

Published: April 9, 2025

Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability industrial systems. At same time, bearing faults a leading cause machinery failures, often resulting in costly downtime, reduced productivity, and, extreme cases, catastrophic damage. This study presents methodology that utilizes Kolmogorov–Arnold Networks—a recent deep learning alternative to Multilayer Perceptrons. The proposed method automatically selects most relevant features from sensor data searches for optimal hyper-parameters within single unified approach. By using shallow network architectures fewer features, models lightweight, easily interpretable, practical real-time applications. Validated on two widely recognized datasets fault diagnosis, framework achieved perfect F1-Scores detection high severity classification tasks, including 100% cases. Notably, it demonstrated adaptability by handling diverse types, such as imbalance misalignment, dataset. availability symbolic representations provided model interpretability, while feature attribution offered insights into types or signals each studied task. These results highlight framework’s potential applications, monitoring, scientific research requiring efficient explainable models.

Language: Английский

Leveraging Large Language Models for Enhancing Safety in Maritime Operations DOI Creative Commons
Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1666 - 1666

Published: Feb. 6, 2025

Maritime operations play a critical role in global trade but face persistent safety challenges due to human error, environmental factors, and operational complexities. This review explores the transformative potential of Large Language Models (LLMs) enhancing maritime through improved communication, decision-making, compliance. Specific applications include multilingual communication for international crews, automated reporting, interactive training, real-time risk assessment. While LLMs offer innovative solutions, such as data privacy, integration, ethical considerations must be addressed. concludes with actionable recommendations insights leveraging build safer more resilient systems.

Language: Английский

Citations

2
A Machine Learning Framework Forpredicting Structural Failures in Shiprecycling: Overcoming Data Gapsacross Recycling Methods DOI

Ini Akpadiaha

Published: Jan. 1, 2025

Language: Английский

Citations

0
A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions DOI Creative Commons
Yashashree Mahale,

Shrikrishna Kolhar,

Anjali S. More

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(4)

Published: March 20, 2025

Language: Английский

Citations

0
Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov–Arnold Networks DOI Creative Commons
Spyros Rigas, Michalis Papachristou,

Ioannis Nektarios Sotiropoulos

et al.

Entropy, Journal Year: 2025, Volume and Issue: 27(4), P. 403 - 403

Published: April 9, 2025

Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability industrial systems. At same time, bearing faults a leading cause machinery failures, often resulting in costly downtime, reduced productivity, and, extreme cases, catastrophic damage. This study presents methodology that utilizes Kolmogorov–Arnold Networks—a recent deep learning alternative to Multilayer Perceptrons. The proposed method automatically selects most relevant features from sensor data searches for optimal hyper-parameters within single unified approach. By using shallow network architectures fewer features, models lightweight, easily interpretable, practical real-time applications. Validated on two widely recognized datasets fault diagnosis, framework achieved perfect F1-Scores detection high severity classification tasks, including 100% cases. Notably, it demonstrated adaptability by handling diverse types, such as imbalance misalignment, dataset. availability symbolic representations provided model interpretability, while feature attribution offered insights into types or signals each studied task. These results highlight framework’s potential applications, monitoring, scientific research requiring efficient explainable models.

Language: Английский

Citations

0