文章基本信息

标题：Joint Estimation of Additive and Parametric Faults: A Model-Based Fault Diagnosis Approach towards Predictive Maintenance
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作者：Koen Classens ; Stan Verbeek ; W.P.M.H. (Maurice) Heemels 等
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