The research is part of the Industry 4.0 approach, which aims to provide modern and advanced analytical tools from "Big Data" and "augmented reality" technologies for improving and optimizing the maintenance of industrial sites geographically distributed around the globe.
Expertises:
Data Science ⊕
Domaine: Manufacturing ⊕
Asset: TSorage ⊕
Factsheet:
The main objective is to create a new platform integrating, on the one hand, algorithms for predictive maintenance 4.0 applicable to similar geographically distributed sites, and on the other hand, a smart help system facilitating the management of maintenance operations by an on-site technician with support of a remote expert, this help system may also suggest actions based on the results of the predictive maintenance algorithm.
This research will have for application case the test bench of aeronautical engines. However, this concept may be used in other applications of geographically distributed infrastructure requiring predictive maintenance 4.0.
Safran Aero Boosters - Department Test cells will develop its test bench experience and exploit the results to provide innovative services that meet the expectations of this market.
Big Bad Wolf will develop and enhance its experience in augmented reality, 3D modeling and WEB platform development.
The objective of CETIC is to broaden its expertise on the specific needs of the industry in terms of new IT technologies and Big Data techniques.
Finally, UCL ICTEAM - PILAB aims to develop its experience in augmented reality and signal processing, industrial applicability studies of new visualization techniques.
The expected results are a time-enriched data analysis platform for geographically distributed test infrastructures, and a technician guidance tool: AR tutorial and AR help system.
These 2 results will be demonstrated on on real cases and in real conditions: by Safran Aero Boosters where the department Test cells will bring all its knowledge in the field of test benches to test the proposed solutions under real conditions, coordinate and direct all research work, and by Big Bad Wolf that will intervene at the interfaces with the user and also for the development of the WB platform.
CETIC will elicit the needs, requirements and constraints to be taken into account in the development of the solution and develop quality data analysis algorithms for the prediction of maintenance operations.
Finally, UCL ICTEAM - PILAB participates in the implementation of tracking in the real-time framework developed, as well as interactive augmented reality procedures of the two experimental validations.
The most important benefits from the Artemtec project is the exploitation of the full potential of "Big Data" and "augmented reality" techniques in the industry. The development of a Maintenance Support Platform with an intelligent assistance system applied to an aircraft engine test bench, will optimize maintenance operations by introducing predictive maintenance tools and also support these operations by augmented reality. The product developed at term would be adaptable according to the needs typical of other industrial sectors.
