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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28223
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dc.contributor.authorGoren Huber, Lilach-
dc.contributor.authorPalmé, Thomas-
dc.contributor.authorArias Chao, Manuel-
dc.date.accessioned2023-07-08T16:34:09Z-
dc.date.available2023-07-08T16:34:09Z-
dc.date.issued2023-06-
dc.identifier.isbn979-8-3503-3875-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28223-
dc.description.abstractThe combination of physics and engineering information with data-driven methods like machine learning (ML) and deep learning is gaining attention in various research fields. One of the promising practical applications of such hybrid methods is for supporting maintenance decision making in the form of condition-based and predictive maintenance. In this paper we focus on the potential of physics-informed data augmentation for ML algorithms. We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. We elaborate on the benefits and prerequisites of each technique and provide guidelines for future practical implementations in other systems.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectPhysics-informed machine learningde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectSolar power plantde_CH
dc.subjectGas turbinede_CH
dc.subjectAircraft enginede_CH
dc.subjectFault detectionde_CH
dc.subjectFault diagnosticsde_CH
dc.subjectRemaining useful lifede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titlePhysics-informed machine learning for predictive maintenance : applied use-casesde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/SDS57534.2023.00016de_CH
dc.identifier.doi10.21256/zhaw-28223-
zhaw.conference.details10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end72de_CH
zhaw.pages.start66de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2023 10th IEEE Swiss Conference on Data Science (SDS)de_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Goren Huber, L., Palmé, T., & Arias Chao, M. (2023). Physics-informed machine learning for predictive maintenance : applied use-cases [Conference paper]. 2023 10th IEEE Swiss Conference on Data Science (SDS), 66–72. https://doi.org/10.1109/SDS57534.2023.00016
Goren Huber, L., Palmé, T. and Arias Chao, M. (2023) ‘Physics-informed machine learning for predictive maintenance : applied use-cases’, in 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 66–72. Available at: https://doi.org/10.1109/SDS57534.2023.00016.
L. Goren Huber, T. Palmé, and M. Arias Chao, “Physics-informed machine learning for predictive maintenance : applied use-cases,” in 2023 10th IEEE Swiss Conference on Data Science (SDS), Jun. 2023, pp. 66–72. doi: 10.1109/SDS57534.2023.00016.
GOREN HUBER, Lilach, Thomas PALMÉ und Manuel ARIAS CHAO, 2023. Physics-informed machine learning for predictive maintenance : applied use-cases. In: 2023 10th IEEE Swiss Conference on Data Science (SDS). Conference paper. IEEE. Juni 2023. S. 66–72. ISBN 979-8-3503-3875-1
Goren Huber, Lilach, Thomas Palmé, and Manuel Arias Chao. 2023. “Physics-Informed Machine Learning for Predictive Maintenance : Applied Use-Cases.” Conference paper. In 2023 10th IEEE Swiss Conference on Data Science (SDS), 66–72. IEEE. https://doi.org/10.1109/SDS57534.2023.00016.
Goren Huber, Lilach, et al. “Physics-Informed Machine Learning for Predictive Maintenance : Applied Use-Cases.” 2023 10th IEEE Swiss Conference on Data Science (SDS), IEEE, 2023, pp. 66–72, https://doi.org/10.1109/SDS57534.2023.00016.


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