Active learning-assisted semi-supervised learning for fault detection and diagnostics with imbalance...
Active learning-assisted semi-supervised learning for fault detection and diagnostics with imbalanced dataset
About this item
Full title
Author / Creator
Publisher
Abingdon: Taylor & Francis
Journal title
Language
English
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Publication information
Publisher
Abingdon: Taylor & Francis
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Scope and Contents
Contents
Data-driven Fault Detection and Diagnostics (FDD) methods often assume that sufficient labeled samples are class-balanced and faulty classes in testing are precedent or seen previously during model training. When monitoring a large fleet of assets at scale, these assumptions may be violated: (I) only a limited number of samples can be manually labe...
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Full title
Active learning-assisted semi-supervised learning for fault detection and diagnostics with imbalanced dataset
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_crossref_primary_10_1080_24725854_2022_2074579
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_crossref_primary_10_1080_24725854_2022_2074579
Other Identifiers
ISSN
2472-5854
E-ISSN
2472-5862
DOI
10.1080/24725854.2022.2074579
