文章基本信息

标题：Supervised Machine Learning for Knowledge-Based Analysis of Maintenance Impact on Profitability
本地全文：下载
作者：Kai Schenkelberg ; Ulrich Seidenberg ; Fazel Ansari 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：10651-10657
DOI：10.1016/j.ifacol.2020.12.2830
语种：English
出版社：Elsevier
摘要：AbstractRecent empirical studies reveal that predictive maintenance is essential for accomplishing business objectives of manufacturing enterprises. Knowledge-based maintenance strategies for optimal operation of industrial machines and physical assets reasonably require explaining and predicting long term economic impacts, based on exploring historical data. This paper examines how supervised machine learning (ML) techniques may enhance anticipating the economic impact of maintenance on profitability (IMP). Planning and monitoring of maintenance activities supported by various statistical learning and supervised ML algorithms have been investigated in the literature of production management. However, data-driven prediction of IMP has not been largely addressed. A novel data-driven framework is proposed comprising cause-and-effect dependencies between maintenance and profitability, which constructs a set of appropriate features as independent variables.
关键词：KeywordsMaintenanceProfitabilitySupervised learningMachine learningRegressionKnowledge-Based Maintenance