Call for abstracts on fleet-based prognostics and health management of industrial assets

Together with imec and the DTAI research group of KU Leuven, the Sirris EluciDATA Lab will be hosting a special session on 'Fleet-based Prognostics and Health Management of Industrial Assets' at the Fifth European Conference of the Prognostics and Health Management Society 2020 in Turin on 1-3 July. Are you involved in research related to this topic? Submit your abstract by 31 January!

Fleet-based analytics aims to intelligently leverage and exploit knowledge across several assets to extract new insights for maintaining and optimising the behaviour of the fleet as a whole, as well as the individual assets that are part of it. Taking this fleet-based knowledge across assets into account also offers new opportunities to improve or extend current state-of-the-art approaches for diagnostics and prognostics related to health monitoring, degradation modelling and benchmarking.


Increasingly, knowledge (such as SCADA data, maintenance logs, etc.) is gathered on fleets of industrial machinery, i.e. sets of (nearly) identical industrial assets deployed in different operating contexts, such as wind or solar parks, steam turbines, heat pumps, compressors, trucks, robots, etc. This information is used by OEMs to plan future maintenance interventions or enhance future product design, and by asset owners, e.g. to monitor their performance.

Understanding and optimising the operational behaviour of such fleets is crucial but challenging, since it often involves complex systems operating in heterogenous and dynamic environments. Leveraging the knowledge that is available from the fleet offers new opportunities to overcome a number of these challenges. Furthermore, it offers new opportunities for improving existing PHM approaches for condition monitoring, diagnostics and prognostics for individual industrial assets by incorporating knowledge on similar assets in the fleet.


The aim of this session is to present and discuss state-of-the-art PHM methodologies and techniques that leverage the knowledge of the fleet, in order to improve the prognostics and health management of industrial assets. Topics of interest include (but are not limited to) diagnostics and prognostics approaches related to health monitoring, degradation modelling and benchmarking within fleets of industrial assets. Also industrial case studies (irrespective of the domain) demonstrating effective solutions tackling the specific challenges and issues in real applications involving fleets of industrial assets are welcome.

Are you doing research related to this topic? Would you like to present your results in a scientific paper at the conference? Get in touch with our experts in charge of the session, Mathias Verbeke and Alessandro Murgia.

You can find more information on the conference here.