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dc.contributor.author
Biggio, Luca
dc.contributor.author
Kastanis, Iason
dc.date.accessioned
2021-03-02T12:17:56Z
dc.date.available
2021-01-21T10:06:08Z
dc.date.available
2021-03-02T12:17:56Z
dc.date.issued
2020-11
dc.identifier.other
10.3389/frai.2020.578613
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/464434
dc.identifier.doi
10.3929/ethz-b-000464434
dc.description.abstract
Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
prognostic and health management
en_US
dc.subject
predictive maintenance
en_US
dc.subject
industry 4.0
en_US
dc.subject
artificial intelligence
en_US
dc.subject
machine learning
en_US
dc.subject
deep learning
en_US
dc.title
Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-11-09
ethz.journal.title
Frontiers in Artificial Intelligence
ethz.journal.volume
3
en_US
ethz.pages.start
578613
en_US
ethz.size
24 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.date.deposited
2021-01-21T10:06:15Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-02T12:18:06Z
ethz.rosetta.lastUpdated
2022-03-29T05:33:00Z
ethz.rosetta.versionExported
true
ethz.COinS
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