Izenburua
Cyber Physical System Based Proactive Collaborative MaintenanceEgilea (beste erakunde batekoa)
Bertsioa
Postprinta
Eskubideak
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksSarbidea
Sarbide irekiaArgitaratzailearen bertsioa
http://dx.doi.org/10.1109/SST.2016.7765654Non argitaratua
Proceedings of the 2016 International Conference on Smart Systems and Technologies (SST). Osijek. 12-14 October. Pp. 173-178. IEEE, 2016Argitaratzailea
IEEEGako-hitzak
Cyber Physical Systems CPSCollaborative maintenance ecosystems
Proactive maintenance
MANTIS
Laburpena
The aim of the MANTIS project is to provide a proactive maintenance service platform architecture based on Cyber
Physical Systems. The platform will allow estimating future performance, predicting an ... [+]
The aim of the MANTIS project is to provide a proactive maintenance service platform architecture based on Cyber
Physical Systems. The platform will allow estimating future performance, predicting and preventing imminent failures and
scheduling proactive maintenance. Maintenance is an important element that creates added value in the business processes and it also creates new business models with a stronger service orientation. Physical systems and the environment they work
in are continuously monitored by a range of intelligent sensors, resulting in massive amounts of data, which characterise the usage history, working condition, location, movement and other physical properties of the systems. These systems are part of a larger network of heterogeneous and collaborative systems (e.g. vehicle fleets) connected via robust communication mechanisms able to operate in challenging environments. MANTIS consists of distributed processing chains that efficiently transform raw data into knowledge, while minimising the need for bandwidth. Sophisticated distributed sensing and decision-making functions are performed at different levels collaboratively, ranging from local nodes to locally optimise performance, bandwidth and maintenance; to cloud-based platforms that integrate information from diverse systems and execute distributed processing and analytics algorithms for global decision-making. [-]