Particle filter-based method for prognostics with application to auxiliary power unit

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DOIResolve DOI: http://doi.org/10.1007/978-3-319-07455-9_21
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TypeBook Chapter
Proceedings titleModern Advances in Applied Intelligence : 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I
Series titleLecture Notes In Computer Science; Volume 8481
Conference27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2014), June 3-6, 2014, Kaohsiung, Taiwan
ISSN0302-9743
ISBN978-3-319-07454-2
978-3-319-07455-9
Pages198207; # of pages: 10
SubjectIntelligent systems; Machinery; Maintenance; Monte Carlo methods; Systems engineering; APU Starter prognostics; Condition-based maintenance (CBM); Data-driven prognostics; Particle filter (PF); remaining useful cycle (RUC); Auxiliary power systems
AbstractParticle filter (PF)-based method has been widely used for machinery condition-based maintenance (CBM), in particular, for prognostics. It is employed to update the nonlinear prediction model for forecasting system states. In this work, we applied PF techniques to Auxiliary Power Unit (APU) prognostics for estimating remaining useful cycle to effectively perform APU health management. After introducing the PF-based prognostic method and algorithms, the paper presents the implementation for APU Starter prognostics along with the experimental results. The results demonstrated that the developed PF-based method is useful for estimating remaining useful cycle for a given failure of a component or a subsystem.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
AffiliationNational Research Council Canada
Peer reviewedYes
NPARC number21272785
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Record identifier9e96282c-ed22-4ec2-b2dd-114de197bf15
Record created2014-12-03
Record modified2016-07-15
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