Call for abstracts – Fleet-based Prognostics and Health Management of Industrial Assets

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The EluciDATA Lab of Sirris together with IMEC and the DTAI research group of the KU Leuven 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 (1-3/07/2020 in Turin) and invite you to submit an abstract related to your research on this topic by 31/01/2020


Fleet-based analytics aims to intelligently leverage and exploit knowledge across several assets to extract new insights for maintaining and optimizing the behavior 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 OEM 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? Then get in touch with our experts in charge of the session Mathias Verbeke and Alessandro Murgia

More information on the conference: here