Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid an...
Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings
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Publisher
Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuato...
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Full title
Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings
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TN_cdi_doaj_primary_oai_doaj_org_article_0a554884e5164385a7c53c06ecbcc0ea
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0a554884e5164385a7c53c06ecbcc0ea
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ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s21175832
