Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional N...
Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network
About this item
Full title
Author / Creator
Wang, Haitao , Yang, Jie , Shi, Lichen and Wang, Ruihua
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
Subjects
More information
Scope and Contents
Contents
The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded features. A large number of recurrent neural network...
Alternative Titles
Full title
Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c4e987a4ad4246c4b1bf825976ea68a4
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c4e987a4ad4246c4b1bf825976ea68a4
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s22239088
