Data-driven predictive maintenance: a methodology primer

Tania Cerquitelli; Nikolaus Nikolakis; Lia Morra; Andrea Bellagarda; Matteo Orlando; Riku Salokangas; Olli Saarela; Jani Hietala; Petri Kaarmila; Enrico Macii

doi:10.1007/978-981-16-2940-2_3

Data-driven predictive maintenance: a methodology primer

Tania Cerquitelli^*, Nikolaus Nikolakis, Lia Morra, Andrea Bellagarda, Matteo Orlando, Riku Salokangas, Olli Saarela, Jani Hietala, Petri Kaarmila, Enrico Macii

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

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.

Original language	English
Title of host publication	Predictive Maintenance in Smart Factories
Subtitle of host publication	Architectures, Methodologies, and Use-cases
Editors	T. Cerquitelli, N. Nikolakis, N. O’Mahony, E. Macii, M. Ippolito, S. Makris
Publisher	Springer
Chapter	8
Pages	39-73
ISBN (Electronic)	978-981-16-2940-2
ISBN (Print)	978-981-16-2939-6
DOIs	https://doi.org/10.1007/978-981-16-2940-2_3
Publication status	Published - 2021
MoE publication type	A3 Part of a book or another research book

Publication series

Series	Information Fusion and Data Science
ISSN	2510-1528

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10.1007/978-981-16-2940-2_3

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Cerquitelli, T., Nikolakis, N., Morra, L., Bellagarda, A., Orlando, M., Salokangas, R., Saarela, O., Hietala, J., Kaarmila, P., & Macii, E. (2021). Data-driven predictive maintenance: a methodology primer. In T. Cerquitelli, N. Nikolakis, N. O’Mahony, E. Macii, M. Ippolito, & S. Makris (Eds.), Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-cases (pp. 39-73). Springer. https://doi.org/10.1007/978-981-16-2940-2_3

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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",

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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",

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Cerquitelli, T, Nikolakis, N, Morra, L, Bellagarda, A, Orlando, M, Salokangas, R, Saarela, O, Hietala, J, Kaarmila, P & Macii, E 2021, Data-driven predictive maintenance: a methodology primer. in T Cerquitelli, N Nikolakis, N O’Mahony, E Macii, M Ippolito & S Makris (eds), Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-cases. Springer, Information Fusion and Data Science, pp. 39-73. https://doi.org/10.1007/978-981-16-2940-2_3

Data-driven predictive maintenance: a methodology primer. / Cerquitelli, Tania; Nikolakis, Nikolaus; Morra, Lia et al.
Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-cases. ed. / T. Cerquitelli; N. Nikolakis; N. O’Mahony; E. Macii; M. Ippolito; S. Makris. Springer, 2021. p. 39-73 (Information Fusion and Data Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

TY - CHAP

T1 - Data-driven predictive maintenance

T2 - a methodology primer

AU - Cerquitelli, Tania

AU - Nikolakis, Nikolaus

AU - Morra, Lia

AU - Bellagarda, Andrea

AU - Orlando, Matteo

AU - Salokangas, Riku

AU - Saarela, Olli

AU - Hietala, Jani

AU - Kaarmila, Petri

AU - Macii, Enrico

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://doi.org/10.1007/978-981-16-2940-2

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DO - 10.1007/978-981-16-2940-2_3

M3 - Chapter or book article

SN - 978-981-16-2939-6

T3 - Information Fusion and Data Science

SP - 39

EP - 73

BT - Predictive Maintenance in Smart Factories

A2 - Cerquitelli, T.

A2 - Nikolakis, N.

A2 - O’Mahony, N.

A2 - Macii, E.

A2 - Ippolito, M.

A2 - Makris, S.

PB - Springer

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Cerquitelli T, Nikolakis N, Morra L, Bellagarda A, Orlando M, Salokangas R et al. Data-driven predictive maintenance: a methodology primer. In Cerquitelli T, Nikolakis N, O’Mahony N, Macii E, Ippolito M, Makris S, editors, Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-cases. Springer. 2021. p. 39-73. (Information Fusion and Data Science). doi: 10.1007/978-981-16-2940-2_3

Data-driven predictive maintenance: a methodology primer

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