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Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder

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Published in:Wind Energy Science
Format: Online Article RSS Article
Published: 2026
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container_title Wind Energy Science
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:22565
institution FRELIP
journal_source_facet Wind Energy Science
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
— — — — — Renewal Energy
Petroleum & Energy
Engineering & Technology
sub_discipline_display Petroleum & Energy
sub_discipline_facet Petroleum & Energy
subject_display — — — — — Renewal Energy
Petroleum & Energy
Engineering & Technology
— — — — — Renewal Energy
Petroleum & Energy
Engineering & Technology
subject_facet — — — — — Renewal Energy
Petroleum & Energy
Engineering & Technology
title Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_auth Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_full Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_fullStr Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_full_unstemmed Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_short Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
title_sort sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder
topic — — — — — Renewal Energy
Petroleum & Energy
Engineering & Technology
url https://doi.org/10.5194/wes-11-1163-2026