Particle filter-based model fusion for prognostics

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-319-19066-2_7
AuthorSearch for: ; Search for: ; Search for: 1
Name affiliation
  1. National Research Council Canada
FormatText
TypeBook 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
Pages63–73
Abstract
Predictive maintenance is an emerging technology which aims at increasing availability of systems, reducing maintenance cost, and ensuring the safety of systems. There exist two main issues in predictive maintenance. The first challenge is the system operation region definition, detection and modelling; and another one is estimation of the remaining useful life (RUL). To address these issues, this paper proposes a particle filter (PF)-based model fusion approach for estimating RUL by classifying the system states into different operation regions in which a data-driven model is developed to estimate RUL corresponding to each region, and combined with PF-based fusion algorithm. This paper reports the proposed approach along with some preliminary results obtained from a case study.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
Peer reviewedYes
NPARC number21276907
Export citationExport as RIS
Report a correctionReport a correction
Record identifierff31b0ff-9218-4390-a0a1-b6ea775dad96
Record created2015-11-10
Record modified2020-03-03
Report a problem on this page
Date modified: