Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified...
Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Fault diagnosis of rotating machinery plays an important role in modern industrial machines. In this paper, a modified sparse Bayesian classification model (i.e., Standard_SBC) is utilized to construct the fault diagnosis system of rotating machinery. The features are extracted and adopted as the input of the SBC-based fault diagnosis system, and t...
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Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model
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TN_cdi_doaj_primary_oai_doaj_org_article_7711da5f9d204bbca8ec468c0c4be1e0
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7711da5f9d204bbca8ec468c0c4be1e0
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ISSN
1099-4300
E-ISSN
1099-4300
DOI
10.3390/e25111549
