A deep reinforcement learning approach for real-time sensor-driven decision making and predictive analytics

Erotokritos Skordilis, Ramin Moghaddass

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The increased complexity of sensor-intensive systems with expensive subsystems and costly repairs and failures calls for efficient real-time control and decision making policies. Deep reinforcement learning has demonstrated great potential in addressing highly complex and challenging control and decision making problems. Despite its potential to derive real-time policies using real-time data for dynamic systems, it has been rarely used for sensor-driven maintenance related problems. In this paper, we propose two novel decision making methods in which reinforcement learning and particle filtering are utilized for (i) deriving real-time maintenance policies and (ii) estimating remaining useful life for sensor-monitored degrading systems. The proposed framework introduces a new direction with many potential opportunities for system monitoring. To demonstrate the effectiveness of the proposed methods, numerical experiments are provided from a set of simulated data and a turbofan engine dataset provided by NASA.

Original languageEnglish (US)
Article number106600
JournalComputers and Industrial Engineering
Volume147
DOIs
StatePublished - Sep 2020

Keywords

  • Decision-making
  • Deep reinforcement learning
  • Particle filters
  • Real-time control
  • Remaining useful life estimation

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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