Industrial Internet of Things-Based Prognostic Health Management: A Mean-Field Stochastic Game Approach

Mohammed Amine Koulali, Sara Koulali, Hamidou Tembine, Abdellatif Kobbane

Research output: Contribution to journalArticle

Abstract

Recent advances in industrial Internet of Things (IIoT) have dramatically leveraged prognostic health management for industrial systems. Indeed, the cognitive and communication capabilities of IIoT empower their integration in the industrial systems maintenance workflow to ease the transition toward industry 4.0. In this paper, we study a mean field stochastic game for IIoT-based CBM of industrial facilities formulated to favor grouped maintenance for cost reduction. We provide an analytical analysis of the proposed game to characterize its equilibrium operating point: mean-field equilibrium (MFE). We design a learning algorithm to reach the MFE based on a local adjustment of the maintenance rate and the global health state distribution of the monitored components. Numerical evaluation validates the proposed game and ensures maintaining a high fraction of the components in a healthy state by acting on preventive and corrective replacement rates.

Original languageEnglish (US)
Article number8471104
Pages (from-to)54388-54395
Number of pages8
JournalIEEE Access
Volume6
DOIs
StatePublished - 2018

Keywords

  • H-learning
  • Markov chain
  • Prognostic health management
  • industrial Internet of Things
  • mean-field equilibrium
  • mean-field stochastic games

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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