MARTIN: An End-to-end Microservice Architecture for Predictive Maintenance in Industry 4.0

Elhabbash, A., Rogoda, K. and Elkhatib, Y. (2023) MARTIN: An End-to-end Microservice Architecture for Predictive Maintenance in Industry 4.0. In: IEEE International Conference on Software Services Engineering (IEEE SSE 2023), Chicago, IL, USA, 02-08 Jul 2023, ISBN 9798350340754 (doi: 10.1109/SSE60056.2023.00013)

Abstract

The amount of data generated in Industry 4.0 and the introduction of advanced data analytics support establishing “smart factories” and one of its crucial characteristics - predictive maintenance. Current solutions primarily focus on offline predictions and do not provide end-to-end scalable solutions. Furthermore, there is a lack of support for incremental ma-chine learning in predictive maintenance. This paper addresses these limitations by proposing MARTIN, a scalable microservice architecture for predictive maintenance that can collect, store, and analyse data, and make decisions based on the machine state. The architecture uses incremental learning as the basis for predictions. The designed system was implemented and its performance was evaluated experimentally. The results show that the solution can provide high prediction accuracy in terms of practical processing time.

• Item Type: Conference Proceedings
• Additional Information: This work was supported in part by the UK EPSRC under grant number EP/R010889/2.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elkhatib, Dr Yehia
• Authors: Elhabbash, A., Rogoda, K., and Elkhatib, Y.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798350340754
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in 2023 IEEE International Conference on Software Services Engineering (SSE)
• Publisher Policy: Reproduced with the permission of the publisher

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
315521		ABC: Adaptive Brokerage for the Cloud	Yehia Elkhatib	Engineering and Physical Sciences Research Council (EPSRC)	EP/R010889/2	Computing Science