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  • 标题:Prognostic Methods for Predictive Maintenance: A generalized Topology
  • 本地全文:下载
  • 作者:Simon Leohold ; Hendrik Engbers ; Michael Freitag
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:1
  • 页码:629-634
  • DOI:10.1016/j.ifacol.2021.08.073
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractPrognostic methods for predictive maintenance have been presented extensively in the literature. While this area’s continuing effort positively affects individual predictive maintenance solutions’ performance and capabilities, a method’s setup remains a big hurdle as the solution space is becoming more complex. The critical settings of a prognostic method are the selection of suitable modeling techniques used for behavior- and condition-modeling, as well as a forecast model for failure prediction. This paper presents a generalized topology of a prognostic method to ease the design of maintenance systems and allow for quicker individual method design and modification. After a broad literature review, the topology and its base components are presented, and an overview of the different kinds of models related to predictive maintenance applications is given.
  • 关键词:KeywordsPrognosticsPredictive MaintenanceCondition MonitoringRemaining Useful Lifetime EstimationMachine Learning
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