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  • 标题:Joint Estimation of Additive and Parametric Faults: A Model-Based Fault Diagnosis Approach towards Predictive Maintenance
  • 本地全文:下载
  • 作者:Koen Classens ; Stan Verbeek ; W.P.M.H. (Maurice) Heemels
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2022
  • 卷号:55
  • 期号:6
  • 页码:304-309
  • DOI:10.1016/j.ifacol.2022.07.146
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe condition of systems, such as production equipment, typically deteriorates over time, increasing the risk of failure and associated unscheduled downtime. Predictive maintenance is a strategy to prevent failure, while maximizing the life cycle of equipment within a system. This paper contributes in this context to the theory of real-time fault diagnosis with an approach that can jointly estimate additive and parametric faults. The proposed fault diagnosis system consists of detection filters which are complemented with a residual evaluator, enabling effective fault isolation and fault estimation for open-loop and closed-loop controlled systems. The effectiveness of this unified approach is illustrated on a mass-spring-damper system.
  • 关键词:KeywordsFault DiagnosisFault DetectionFault IsolationFault EstimationPredictive MaintenanceMechatronic SystemsProduction EquipmentLinear Fractional Representation
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