Distributed diagnostics, prognostics and maintenance planning: realizing industry 4.0

Amit Kumar Jain*, Priyansha Chouksey, Ajith Kumar Parlikad, Bhupesh Kumar Lad

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In this paper, a novel distributed yet integrated approach for diagnostics and prognostics is presented. An experimental study is conducted to validate the performance. Results showed that distributed prognostics give better performance in leaser computational time. Also, the proposed approach helps in making the results of the machine learning techniques comprehensible and more accurate. These results will be handy in arriving at predictive maintenance schedule considering the criticality of the system, the dependency of the components, available maintenance resources and confidence level in the results of the prognostic.
Original languageEnglish
Title of host publication4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies - AMEST 2020
EditorsAjith Parlikad, Christos Emmanouilidis, Benoit Iung, Marco Macchi
PublisherInternational Federation of Automatic Control (IFAC)
Pages345-359
Number of pages6
DOIs
Publication statusPublished - 18 Dec 2020
Event4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies - Cambridge, United Kingdom
Duration: 10 Sep 202011 Sep 2020
https://www.amest2020.eng.cam.ac.uk/

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
PublisherInternational Federation of Automatic Control (IFAC)
ISSN (Electronic)2405-8963

Conference

Conference4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies
Abbreviated titleAMEST 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period10/09/2011/09/20
Internet address

Keywords

  • diagnostic
  • distributed approach
  • industry 4.0
  • predictive maintenance planning
  • prognostic

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