@inbook{d9ce82eb2f14429ab0da878d9bac83ae,
title = "Data-driven predictive maintenance: a methodology primer",
abstract = "Predictive maintenance aims at proactively assessing the current condition of assets and performing maintenance activities if and when needed to preserve them in the optimal operational condition. This in turn may lead to a reduction of unexpected breakdowns and production stoppages as well as maintenance costs, ultimately resulting in reduced production costs. Empowered by recent advances in the fields of information and communication technologies and artificial intelligence, this chapter attempts to define the main operational blocks for predictive maintenance, building upon existing standards discusses and key data-driven methodologies for predictive maintenance. In addition, technical information related to potential data models for storing and communicating key information are provided, finally closing the chapter with different deployment strategies for predictive analytics as well as identifying open issues.",
author = "Tania Cerquitelli and Nikolaus Nikolakis and Lia Morra and Andrea Bellagarda and Matteo Orlando and Riku Salokangas and Olli Saarela and Jani Hietala and Petri Kaarmila and Enrico Macii",
year = "2021",
doi = "10.1007/978-981-16-2940-2_3",
language = "English",
isbn = "978-981-16-2939-6",
series = "Information Fusion and Data Science",
publisher = "Springer",
pages = "39--73",
editor = "T. Cerquitelli and N. Nikolakis and N. O{\textquoteright}Mahony and E. Macii and { Ippolito}, M. and S. Makris",
booktitle = "Predictive Maintenance in Smart Factories",
address = "Germany",
}