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Big-data-driven based intelligent prognostics scheme in industry 4.0 environment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a big-data-driven based intelligent prognostics strategy is proposed to deal with industrial big data generated in the process of intelligent manufacturing, which is an inevitable trend in the industry 4.0 environment. The developed scheme demonstrated the important issues for the intelligent prognostics methodology, including pre-processing methods for industrial big data, association analysis based feature processing, and deep learning based prognostics model, spark platform based parallel computing, etc. The proposed methodology and technical system will provide important referential value for the construction of big-data-driven machine prognostics system in industry 4.0 environment.

Original languageEnglish
Title of host publication2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings
EditorsBin Zhang, Yu Peng, Haitao Liao, Datong Liu, Shaojun Wang, Qiang Miao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538603703
DOIs
StatePublished - 20 Oct 2017
Externally publishedYes
Event8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017 - Harbin, China
Duration: 9 Jul 201712 Jul 2017

Publication series

Name2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings

Conference

Conference8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017
Country/TerritoryChina
CityHarbin
Period9/07/1712/07/17

Keywords

  • association analysis
  • deep learning
  • industrial big data
  • parallel computing
  • prognostics model

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