{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T14:27:43Z","timestamp":1771943263328,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT-Funda\u00e7\u00e3o para a ci\u00eancia e Tecnologia","award":["2020.06926.BD"],"award-info":[{"award-number":["2020.06926.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3\u20135% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.<\/jats:p>","DOI":"10.3390\/fi16060214","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T11:14:16Z","timestamp":1718622856000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8001-2172","authenticated-orcid":false,"given":"Pedro","family":"Nunes","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Centre for Mechanical Technology and Automation, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3628-6795","authenticated-orcid":false,"given":"Eug\u00e9nio","family":"Rocha","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Center for Research and Development in Mathematics and Applications (CIDMA), 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0417-8167","authenticated-orcid":false,"given":"Jos\u00e9","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Centre for Mechanical Technology and Automation, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"ref_1","first-page":"2169054","article-title":"Joint optimisation of the maintenance and buffer stock policies considering back orders","volume":"10","author":"Aghdam","year":"2023","journal-title":"Int. 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