Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Abstract

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases.

Keywords:

• Condition monitoring
• Industry 4.0
• intelligent maintenance systems
• Internet of Things
• predictive maintenance

Acknowledgments

The authors would like to express their gratitude to Plastic Omnium Clean Energy Systems, for sponsoring the work presented in this paper.

Data availability statement

The data that support the findings of this study are available from the corresponding author, VC, upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Alphatech: research centre of Plastic Omnium CES, located in Compiègne – France

Additional information

Notes on contributors

Vincent Ciancio

Vincent Ciancio is a PhD student in industrial engineering at Ecole Nationale Supérieure d'Arts et Métiers (ENSAM), France. He obtained his engineering diploma from ENSAM in 2018, a Master of Science in production engineering and management from KTH, Stockholm, and a Master of Science in knowledge integration in mechanical production, design and manufacturing from ENSAM. His research interests include Industry 4.0 topics, mainly related to predictive maintenance.

Lazhar Homri

Lazhar Homri is an associate professor at Arts et Métiers Institute of Technology since 2015. He obtained his Master of applied mathematics from Aix Marseille university in 2011 and his PhD in mechanics and engineering from Bordeaux University in 2014. His research interests include Integrated Product and Process design, Tolerancing, Uncertainty management and AI.

Jean-Yves Dantan

Jean-Yves Dantan is a full professor at 'Arts et Métiers Campus Metz' since 2010. He obtained his Master of Science in Manufacturing and Industrial engineering from ‘Ecole Normale Supérieure de Paris Saclay’ (LURPA) in 1996, his PhD from University of Bordeaux in 2000 and his Habilitation thesis from ‘Ecole Normale Supérieure de Paris Saclay’ in 2009. His research interests include Integrated Product and Process Design, Tolerancing, Uncertainty management and CAPP. He has published around 150 papers and communications. He is a CIRP member since 2011. He is the coordinator of French-German Institute for ‘Industry of the Future’ (Institute between Karlsruhe Institute of Technology & Arts et Métiers) and of the French-German Doctoral college. He is the coordinator of several research projects: AdeQuat, RobustAM, Industrial chair, FlexSpeedFactory, etc.

Ali Siadat

Ali Siadat received his PhD in robotics and digital signal processing from Institut National Polytechnique de Lorraine in France. He started his carrier as an associate professor in computer and industrial engineering at Arts et Metiers and became a full professor in 2016 at the same engineering school. He was the head of applied mathematics and computer engineering department during 7 years and since 2019, he's the director of Design, Manufacturing and Control laboratory. He served also as a guest professor in several universities in China, Iran, Morocco and Brazil. His research interests include artificial intelligence, information system, knowledge formalisation and operations research applied to production system fields. He has published over than 60 papers in distinguished scientific journals and more than 80 papers in international conferences and he supervised more than 25 PhD theses. Ali Siadat is actively involved in Factories of the Future programme in France and his research activities are driven usually with international collaborations and very close to industries. His new research projects interest in integrated human factors to the optimisation of production systems.