Fault diagnosis and planning optimisation within an e-maintenance framework
06-11-2020
Tekniker
Eduardo Gilabert, a researcher working for the Smart Information Systems Unit at the technology centre, has presented his thesis “Fault diagnosis and planning optimisation in an e-maintenance framework" to demonstrate how potential improvements can be brought about by techniques and methodologies associated related to prescriptive analytics with regard to industrial maintenance applications.
Quality improvements, shorter response times and continuous changes in the demand for industrial services and products have upgraded the performance of operations and maintenance, although there is still a long way to go yet to achieve complete optimisation in many areas of work.
Eduardo Gilabert, a researcher working for the Smart Information Systems Unit at Tekniker, a member of the Basque Research and Technology Alliance (BRTA), has presented his thesis entitled “Fault diagnosis and planning within an e-maintenance framework” to prove how potential improvements can be brought about by techniques and methodologies related to prescriptive analytics in industrial maintenance applications.
His research has specifically focused specifically on three different areas: e-maintenance and interoperability, error diagnosis, strategy simulation and planning optimisation.
As regards e-maintenance, it is an area that is fundamentally focused on developing collaborative and smart platforms to incorporate of an extensive range of technological tools (sensors, communication systems, storage and analysis methods, etc.) that offer the possibility of incorporating continuous improvements to optimise assets and processes and further interoperability between systems.
As far as error diagnosis is concerned, the thesis focuses on the methodology to be followed and the actions required to set up a reliable diagnostic system whenever data are unavailable or scarce.
Lastly, and with regard to simulating strategies and optimising planning actions, the study addresses technologies that make it possible to optimise maintenance strategies by using more reliable designs or improving maintenance decision making.
This thesis has been codirected by Basilio Sierra Araujo, a professor at the Department of Computational Science and Artificial Intelligence of the University of the Basque Country (UPV/EHU) and by Aitor Arnaiz, director of the Tekniker Smart Information Systems Unit.