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

Ioannis Nektarios Sotiropoulos

и другие.

Entropy, Год журнала: 2025, Номер 27(4), С. 403 - 403

Опубликована: Апрель 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.

Язык: Английский

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

Ioannis Nektarios Sotiropoulos

и другие.

Entropy, Год журнала: 2025, Номер 27(4), С. 403 - 403

Опубликована: Апрель 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.

Язык: Английский

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