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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorZgraggen, Jannik-
dc.contributor.authorGuo, Yuyan-
dc.contributor.authorNotaristefano, Antonio-
dc.contributor.authorGoren Huber, Lilach-
dc.date.accessioned2023-11-12T14:57:25Z-
dc.date.available2023-11-12T14:57:25Z-
dc.date.issued2023-09-
dc.identifier.isbn978-981-18-8071-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29066-
dc.description.abstractMachine learning algorithms for anomaly detection often assume training with historical data gathered under normal conditions, and detect anomalies based on large residuals at inference time. In real-world applications, labelled anomaly-free data is most often unavailable. In fact, a common situation is that the training data is contaminated with an unknown fraction of anomalies or faults of the same type we aim to detect. In this case, training residual-based models with the contaminated data often leads to increased missed detections and/or false alarms. While this challenge is rather common, in particular in technical fault detection setups, it is only rarely addressed in the scientific literature. In this paper we address this problem by introducing a data refinement algorithm that is capable of cleaning the contaminated training data in a fully unsupervised manner, and apply the algorithm to a problem of fault detection in grid-scale solar power plants. The data refinement framework is based on an original physics informed deep learning classification algorithm that would require healthy data as its input, in order to generate from it synthetic faulty data and train a binary classifier. We show that in order to achieve high fault detection performance, it is essential to avoid contamination of the original healthy data with unlabelled faults. To this end, we introduce an algorithm that isolates the healthy data in a fully unsupervised manner prior to training the binary classifier. We test our algorithm with field data from an operational solar power plant which includes contamination of unlabelled faulty data and demonstrate its high performance. In addition, we demonstrate the robustness of the proposed refinement method against an increasing fraction of faults in the training data.de_CH
dc.language.isoende_CH
dc.publisherResearch Publishingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDeep learningde_CH
dc.subjectPhysics informed machine learningde_CH
dc.subjectSolar powerde_CH
dc.subjectAnomaly detectionde_CH
dc.subjectUnsupervised machine learningde_CH
dc.subjectFault detectionde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectData contaminationde_CH
dc.subjectData refinementde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleFully unsupervised fault detection in solar power plants using physics-informed deep learningde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeSingaporede_CH
dc.identifier.doi10.3850/978-981-18-8071-1_P652-cdde_CH
zhaw.conference.details33rd European Safety and Reliability Conference (ESREL), Southampton, United Kingdom, 3-7 September 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1745de_CH
zhaw.pages.start1737de_CH
zhaw.parentwork.editorBrito, Mário P.-
zhaw.parentwork.editorAven, Terje-
zhaw.parentwork.editorBaraldi, Piero-
zhaw.parentwork.editorČepin, Marko-
zhaw.parentwork.editorZio, Enrico-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 33rd European Safety and Reliability Conference (ESREL 2023)de_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawIntelligente Diagnostik von Leistungseinbussen in Solarkraftwerkende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Zgraggen, J., Guo, Y., Notaristefano, A., & Goren Huber, L. (2023). Fully unsupervised fault detection in solar power plants using physics-informed deep learning [Conference paper]. In M. P. Brito, T. Aven, P. Baraldi, M. Čepin, & E. Zio (Eds.), Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023) (pp. 1737–1745). Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P652-cd
Zgraggen, J. et al. (2023) ‘Fully unsupervised fault detection in solar power plants using physics-informed deep learning’, in M.P. Brito et al. (eds) Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023). Singapore: Research Publishing, pp. 1737–1745. Available at: https://doi.org/10.3850/978-981-18-8071-1_P652-cd.
J. Zgraggen, Y. Guo, A. Notaristefano, and L. Goren Huber, “Fully unsupervised fault detection in solar power plants using physics-informed deep learning,” in Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023), Sep. 2023, pp. 1737–1745. doi: 10.3850/978-981-18-8071-1_P652-cd.
ZGRAGGEN, Jannik, Yuyan GUO, Antonio NOTARISTEFANO und Lilach GOREN HUBER, 2023. Fully unsupervised fault detection in solar power plants using physics-informed deep learning. In: Mário P. BRITO, Terje AVEN, Piero BARALDI, Marko ČEPIN und Enrico ZIO (Hrsg.), Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023). Conference paper. Singapore: Research Publishing. September 2023. S. 1737–1745. ISBN 978-981-18-8071-1
Zgraggen, Jannik, Yuyan Guo, Antonio Notaristefano, and Lilach Goren Huber. 2023. “Fully Unsupervised Fault Detection in Solar Power Plants Using Physics-Informed Deep Learning.” Conference paper. In Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023), edited by Mário P. Brito, Terje Aven, Piero Baraldi, Marko Čepin, and Enrico Zio, 1737–45. Singapore: Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P652-cd.
Zgraggen, Jannik, et al. “Fully Unsupervised Fault Detection in Solar Power Plants Using Physics-Informed Deep Learning.” Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023), edited by Mário P. Brito et al., Research Publishing, 2023, pp. 1737–45, https://doi.org/10.3850/978-981-18-8071-1_P652-cd.


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