Particle filter-based approach to estimate remaining useful life for predictive maintenance

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-319-19066-2_67
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Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
  2. National Research Council of Canada
FormatText, Book chapter
Proceedings titleCurrent Approaches in Applied Artificial Intelligence: 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings
Series titleLecture Notes in Computer Science; no. 9101
Conference28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2015), June 10-12, 2015, Seoul, South Korea
ISSN0302-9743
ISBN978-3-319-19065-5
978-3-319-19066-2
Pages692–701
Subjectparticle filter (PF); remaining useful life (RUL); predictive maintenance (PM); predictive model; APU starter prognostics
Abstract
Estimation of remaining useful life (RUL) plays a vital role in performing predictive maintenance for complex systems today. However, it still remains a challenge. To address this issue, we propose a Particle filter (PF)- based method to estimate remaining useful life for predictive maintenance by employing PF technique to update the nonlinear predictive models for forecasting system states. In particular, we applied PF techniques to estimate remaining useful life by integrating data-driven modeling techniques in order to effectively perform predictive maintenance. After introducing the PF-based algorithm, the paper presents the implementation along with the experimental results through a case study of Auxiliary Power Unit (APU) starter prognostics. The results demonstrated that the developed method is useful for estimating RUL for predictive maintenance.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
Peer reviewedYes
NPARC number21276982
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Record identifier89c8f0e2-94c6-42e0-acec-34f6d7a605aa
Record created2015-11-10
Record modified2020-06-11
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