{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:17:48Z","timestamp":1782407868686,"version":"3.54.5"},"reference-count":70,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. This study presents a methodology that utilizes Kolmogorov\u2013Arnold Networks\u2014a recent deep learning alternative to Multilayer Perceptrons. The proposed method automatically selects the most relevant features from sensor data and searches for optimal hyper-parameters within a single unified approach. By using shallow network architectures and fewer features, the resulting models are lightweight, easily interpretable, and practical for real-time applications. Validated on two widely recognized datasets for bearing fault diagnosis, the framework achieved perfect F1-Scores for fault detection and high performance in fault and severity classification tasks, including 100% F1-Scores in most cases. Notably, it demonstrated adaptability by handling diverse fault types, such as imbalance and misalignment, within the same dataset. The availability of symbolic representations provided model interpretability, while feature attribution offered insights into the optimal feature types or signals for each studied task. These results highlight the framework\u2019s potential for practical applications, such as real-time machinery monitoring, and for scientific research requiring efficient and explainable models.<\/jats:p>","DOI":"10.3390\/e27040403","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T07:49:02Z","timestamp":1744184942000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov\u2013Arnold Networks"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2352-8709","authenticated-orcid":false,"given":"Spyros","family":"Rigas","sequence":"first","affiliation":[{"name":"Department of Digital Industry Technologies, School of Science, National and Kapodistrian University of Athens, 34400 Psachna, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5650-4206","authenticated-orcid":false,"given":"Michalis","family":"Papachristou","sequence":"additional","affiliation":[{"name":"Department of Physics, School of Science, National and Kapodistrian University of Athens, 15784 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0832-3003","authenticated-orcid":false,"given":"Ioannis","family":"Sotiropoulos","sequence":"additional","affiliation":[{"name":"School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-8292","authenticated-orcid":false,"given":"Georgios","family":"Alexandridis","sequence":"additional","affiliation":[{"name":"Department of Digital Industry Technologies, School of Science, National and Kapodistrian University of Athens, 34400 Psachna, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1109\/TIM.2018.2806984","article-title":"Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery","volume":"67","author":"Song","year":"2018","journal-title":"IEEE Trans. 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