Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pizza, Gianmarco | - |
dc.contributor.author | Notaristefano, Antonio | - |
dc.contributor.author | Fabbri, Gregory Sean | - |
dc.contributor.author | Goren Huber, Lilach | - |
dc.date.accessioned | 2021-02-04T11:13:46Z | - |
dc.date.available | 2021-02-04T11:13:46Z | - |
dc.date.issued | 2020-06-08 | - |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/21546 | - |
dc.description.abstract | Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data Acquisition (SCADA) system, while readily available alarms and warnings from the SCADA system are typically not utilized. In this work we present a novel Artificial Intelligence (AI) based approach for early fault detection of wind turbines using alarms and warnings from the SCADA system. | de_CH |
dc.language.iso | en | de_CH |
dc.publisher | WindEurope | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | Fault detection | de_CH |
dc.subject | Predictive maintenance | de_CH |
dc.subject | Artificial intelligence | de_CH |
dc.subject | Machine learning | de_CH |
dc.subject | Wind turbines | de_CH |
dc.subject | SCADA data | de_CH |
dc.subject | Error logs | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.subject.ddc | 620: Ingenieurwesen | de_CH |
dc.title | An AI-based fault detection model using alarms and warnings from the SCADA system | de_CH |
dc.type | Konferenz: Poster | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
zhaw.conference.details | WindEurope Technology Workshop 2020 : Resource Assessment & Analysis of Operating Wind Farms, online, 8-11 June 2020 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.publication.review | Peer review (Abstract) | de_CH |
zhaw.title.proceedings | Proceedings of the WindEurope Technology Workshop 2020 | de_CH |
zhaw.funding.zhaw | Machine Learning Based Fault Detection for Wind Turbines | de_CH |
zhaw.author.additional | No | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen School of Engineering |
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Pizza, G., Notaristefano, A., Fabbri, G. S., & Goren Huber, L. (2020, June 8). An AI-based fault detection model using alarms and warnings from the SCADA system. Proceedings of the WindEurope Technology Workshop 2020.
Pizza, G. et al. (2020) ‘An AI-based fault detection model using alarms and warnings from the SCADA system’, in Proceedings of the WindEurope Technology Workshop 2020. WindEurope.
G. Pizza, A. Notaristefano, G. S. Fabbri, and L. Goren Huber, “An AI-based fault detection model using alarms and warnings from the SCADA system,” in Proceedings of the WindEurope Technology Workshop 2020, Jun. 2020.
PIZZA, Gianmarco, Antonio NOTARISTEFANO, Gregory Sean FABBRI und Lilach GOREN HUBER, 2020. An AI-based fault detection model using alarms and warnings from the SCADA system. In: Proceedings of the WindEurope Technology Workshop 2020. Conference poster. WindEurope. 8 Juni 2020
Pizza, Gianmarco, Antonio Notaristefano, Gregory Sean Fabbri, and Lilach Goren Huber. 2020. “An AI-Based Fault Detection Model Using Alarms and Warnings from the SCADA System.” Conference poster. In Proceedings of the WindEurope Technology Workshop 2020. WindEurope.
Pizza, Gianmarco, et al. “An AI-Based Fault Detection Model Using Alarms and Warnings from the SCADA System.” Proceedings of the WindEurope Technology Workshop 2020, WindEurope, 2020.
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