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

• Published in: International journal of production research Vol. 62; no. 6; pp. 2255 - 2271
• Main Authors: Ciancio, Vincent, Homri, Lazhar, Dantan, Jean-Yves, Siadat, Ali
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18.03.2024; Taylor & Francis LLC
• Subjects: Automobile industry; Communication; Condition monitoring; Control equipment; Data management; Engineering Sciences; Industry 4.0; intelligent maintenance systems; Internet of Things; Predictive maintenance; Repair & maintenance; Condition monitoring; Industry 4.0; intelligent maintenance systems; Internet of Things; predictive maintenance
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2217293

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.