Dataset Identification:
Resource Abstract:
- Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics.
Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI)
community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because
prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of
systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to
our previously published survey of data-driven prognostics.
Citation
- Title A Survey of Artificial Intelligence for Prognostics
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- revision Date
2013-05-13T09:36:47
Resource language:
[u'en-US']
Constraints on resource usage:
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- Constraints
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- Use limitation statement:
- public
point of contact
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publisher
- individual Name {u'hasEmail': u'mailto:miryam.strautkalns@nasa.gov', u'fn': u'Miryam Strautkalns'}
- organisation Name
{u'subOrganizationOf': {u'subOrganizationOf': {u'name': u'U.S. Government'}, u'name': u'National Aeronautics and Space Administration'},
u'name': u'Dashlink'}
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- Contact information
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- Address
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- electronic Mail Address
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Metadata data stamp:
2013-05-13T09:36:47
Metadata contact
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publisher
Metadata scope code
dataset
Metadata standard for this record:
ISO 19115:2003 - Geographic information - Metadata
standard version:
ISO 19115:2003
Metadata record identifier:
DASHLINK_744
Metadata record format is ISO19139 XML (MD_Metadata)