Application of machine learning and rough set theory in lean maintenance decision support system development

Katarzyna Antosz, Małgorzata Jasiulewicz-Kaczmarek, Łukasz Paśko, Chao Zhang, Shaoping Wang
<span title="2021-09-14">2021</span> <i title="Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4q7bbpngbfbmrnyukumucw4shu" style="color: black;">Eksploatacja i Niezawodnosc</a> </i> &nbsp;
Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company's operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance
more &raquo; ... on improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17531/ein.2021.4.12">doi:10.17531/ein.2021.4.12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5erlvdfwszdc7gcvtnht5y6eoi">fatcat:5erlvdfwszdc7gcvtnht5y6eoi</a> </span>
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