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Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines

Detalles Bibliográficos
Autores principales: Nandi, Asoke K, Ahmed, Hosameldin
Lenguaje:eng
Publicado: John Wiley & Sons 2020
Materias:
Acceso en línea:http://cds.cern.ch/record/2705155
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author Nandi, Asoke K
Ahmed, Hosameldin
author_facet Nandi, Asoke K
Ahmed, Hosameldin
author_sort Nandi, Asoke K
collection CERN
id cern-2705155
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
publisher John Wiley & Sons
record_format invenio
spelling cern-27051552021-04-21T18:12:37Zhttp://cds.cern.ch/record/2705155engNandi, Asoke KAhmed, HosameldinCondition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machinesEngineeringJohn Wiley & Sonsoai:cds.cern.ch:27051552020
spellingShingle Engineering
Nandi, Asoke K
Ahmed, Hosameldin
Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title_full Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title_fullStr Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title_full_unstemmed Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title_short Condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
title_sort condition monitoring with vibration signals: compressive sampling and learning algorithms for rotating machines
topic Engineering
url http://cds.cern.ch/record/2705155
work_keys_str_mv AT nandiasokek conditionmonitoringwithvibrationsignalscompressivesamplingandlearningalgorithmsforrotatingmachines
AT ahmedhosameldin conditionmonitoringwithvibrationsignalscompressivesamplingandlearningalgorithmsforrotatingmachines