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dc.contributor.author
Arias Chao, Manuel
dc.contributor.author
Kulkarni, Chetan
dc.contributor.author
Goebel, Kai
dc.contributor.author
Fink, Olga
dc.date.accessioned
2021-02-04T16:14:04Z
dc.date.available
2021-02-04T03:56:46Z
dc.date.available
2021-02-04T16:14:04Z
dc.date.issued
2021-01
dc.identifier.other
10.3390/data6010005
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/467626
dc.identifier.doi
10.3929/ethz-b-000467626
dc.description.abstract
A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
CMAPSS
en_US
dc.subject
run-to-failure
en_US
dc.subject
prognostics
en_US
dc.title
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-01-13
ethz.journal.title
Data
ethz.journal.volume
6
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
5
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Data-Driven Intelligent Predictive Maintenance of Industrial Assets
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
ethz.grant.agreementno
176878
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
SNF-Förderungsprofessuren Stufe 2
ethz.date.deposited
2021-02-04T03:56:54Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-02-04T16:14:17Z
ethz.rosetta.lastUpdated
2024-02-02T13:02:25Z
ethz.rosetta.versionExported
true
ethz.COinS
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