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

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

doi:10.1109/ACCESS.2018.2871859

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 journal › Article › peer-review

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 language	English (US)
Article number	8471104
Pages (from-to)	54388-54395
Number of pages	8
Journal	IEEE Access
Volume	6
DOIs	https://doi.org/10.1109/ACCESS.2018.2871859
State	Published - 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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10.1109/ACCESS.2018.2871859

Cite this

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

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.",

keywords = "H-learning, Markov chain, Prognostic health management, industrial Internet of Things, mean-field equilibrium, mean-field stochastic games",

author = "Koulali, {Mohammed Amine} and Sara Koulali and Hamidou Tembine and Abdellatif Kobbane",

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year = "2018",

doi = "10.1109/ACCESS.2018.2871859",

language = "English (US)",

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AU - Tembine, Hamidou

AU - Kobbane, Abdellatif

PY - 2018

Y1 - 2018

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AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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