{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T12:23:24Z","timestamp":1775478204459,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfeicoamento de Pessoal de N\u00edvel Superior"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine-learning-based models for the tasks of fault detection, classification, and severity estimation. To enhance the effectiveness of fault diagnosis, information acquired in one phase is leveraged in the subsequent phase. Additionally, in the pursuit of attaining models that are both compact and efficient, an explainable artificial intelligence (XAI) technique is incorporated to meticulously select optimal features for the machine learning (ML) models. The chosen ML technique for the tasks of fault detection, classification, and severity estimation is the support vector machine (SVM). To validate the approach, the widely recognized Case Western Reserve University benchmark is utilized. The results obtained emphasize the efficiency and efficacy of the proposal. Remarkably, even with a highly limited number of features, evaluation metrics consistently indicate an accuracy of over 90% in the majority of cases when employing this approach.<\/jats:p>","DOI":"10.3390\/make6010016","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T08:28:16Z","timestamp":1707294496000},"page":"316-341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"6","author":[{"given":"Mailson Ribeiro","family":"Santos","sequence":"first","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Technology Center, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2690-1563","authenticated-orcid":false,"given":"Affonso","family":"Guedes","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation, Federal University of Rio Grande Do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}]},{"given":"Ignacio","family":"Sanchez-Gendriz","sequence":"additional","affiliation":[{"name":"Laboratory for Technological Innovation in Health (LAIS), Hospital Universit\u00e1rio Onofre Lopes, Federal University of Rio Grande Do Norte (UFRN), Natal 59078-970, Rio Grande do Norte, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"ref_1","first-page":"5060","article-title":"Reliability Analysis of Ball Bearing on the Crankshaft of Piston Compressors","volume":"22","author":"Desnica","year":"2016","journal-title":"J. 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