@article{f8787e74d3374853809dd7ff571aae15,
title = "Advanced sensor-based maintenance in real-world exemplary cases",
abstract = "Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.",
keywords = "Condition-based maintenance, demonstrators, pilots, predictive maintenance, use cases, virtual sensors",
author = "Michele Albano and {Lino Ferreira}, Luis and {Di Orio}, Giovanni and Pedro Mal{\'o} and Godfried Webers and Erkki Jantunen and Iosu Gabilondo and Mikel Viguera and Gregor Papa",
note = "Funding Information: This work was partially supported by National Funds through FCT/MEC (Portuguese Foundation for Science and Technology), Slovenian Research Agency (research core funding No. P2-0098), Finnish Funding Agency for Innovation Tekes, and co-financed by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (PCEC/04234); also by the EU ECSEL JU under the H2020 Framework Programme, within project ECSEL/0004/2014, JU grant no. 662189 (MANTIS); also by EU ECSEL JU under the H2020 Framework Programme, JU grant no. 737459 (Productive4.0 project). This work was partially supported by National Funds through FCT/MEC (Portuguese Foundation for Science and Technology), Slovenian Research Agency (research core funding No. P2-0098), Finnish Funding Agency for Innovation Tekes, and co-financed by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (CEC/04234); also by FCT/MEC and the EU ECSEL JU under the H2020 Framework Programme, within project ECSEL/0004/2014, JU grant no. 662189 (MANTIS); also by EU ECSEL JU under the H2020 Framework Programme, JU grant no. 737459 (Productive4.0 project). Funding Information: This work was partially supported by National Funds through FCT/MEC (Portuguese Foundation for Science and Technology), Slovenian Research Agency (research core funding No. P2-0098), Finnish Funding Agency for Innovation Tekes, and co-financed by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (CEC/04234); also by FCT/MEC and the EU ECSEL JU under the H2020 Framework Programme, within project ECSEL/0004/2014, JU grant no. 662189 (MANTIS); also by EU ECSEL JU under the H2020 Framework Programme, JU grant no. 737459 (Productive4.0 project). Publisher Copyright: {\textcopyright} 2020, {\textcopyright} 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
doi = "10.1080/00051144.2020.1794192",
language = "English",
volume = "61",
pages = "537--553",
journal = "Automatika",
issn = "0005-1144",
publisher = "Taylor & Francis",
number = "4",
}