Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction
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Title
Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction
Authors
Keywords
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Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 119, Issue 3-4, Pages 2059-2076
Publisher
Springer Science and Business Media LLC
Online
2021-12-02
DOI
10.1007/s00170-021-08392-6
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- A residual-based Gaussian process model framework for finite element model updating
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