Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution

Simon Zhai, Meltem Göksu Kandemir, Gunther Reinhart

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

To harness the full potential of predictive maintenance (PdM), PdM information has to be used to optimally plan production and maintenance actions. Hence, operation-specific modelling of degradation, i.e. predictions of the health condition under time-varying operational conditions, has to be realized. By utilizing operation-specific degradation information, maintenance and production can be planned with regard to each other and thus, predictive maintenance integrated production scheduling (PdM-IPS) is enabled. This publication proposes a novel PdM-IPS approach consisting of two interacting modules: an operation-specific Prognostics and Health Management (PHM) module and an integrated production scheduling and maintenance planning (IPSMP) module. Specifically, the mathematical problem of the IPSMP module based on an extended version of the maintenance integrated flexible job shop problem is formulated. A two-stage genetic algorithm to efficiently solve this problem is designed and subsequently applied to simulated condition monitoring, as well as real industrial data. Results indicate that the approach is able to find feasible high quality PdM integrated production schedules.

Original languageEnglish
Pages (from-to)65-88
Number of pages24
JournalProduction Engineering
Volume16
Issue number1
DOIs
StatePublished - Feb 2022

Keywords

  • Decision support
  • Genetic algorithm
  • Integrated scheduling
  • Predictive maintenance

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