Keywords: data, data model, open platform, digital twin, railway infrastucture, ETL, condition-based maintenance, predictive maintenance, preventive maintenance.

Maintenance has become a critical issue across all sectors of industry. It is not only a technical necessity but also a major, if not the primary, cost driver. With the advancement of technology, maintenance processes are increasingly driven by data collection. Vast amounts of data are gathered, stored, and analysed to provide valuable management insights. This data comes in various formats and encompasses multiple relationships, making proper structuring essential. The data structure must thus be as open as possible, meaning it should be extensible and capable of integrating new data types. Drawing from the feedback and experience gained through our extensive work with the major French railway companies, we have developed a generic and innovative data model. This model supports a cutting-edge tool we call the Intelligent Digital Data Twin (IDDT, or Preditic-IDDT in reference to our company's name) for predictive and preventive maintenance. Unlike traditional Digital Twins, Preditic-IDDT focuses on the data flow surrounding the system under study rather than the physical system itself.

download the full-text of this paper (PDF, 11 pages, 465 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description