Dataset Identification:
Resource Abstract:
- The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft
have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for
the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing
gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process
consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The
data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate
time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can
identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional
flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts.
This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based
analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods
used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.
Citation
- Title Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms
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- revision Date
2014-09-08T15:18:00
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:bryan.l.matthews@nasa.gov', u'fn': u'Bryan Matthews'}
- 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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- electronic Mail Address
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Metadata data stamp:
2014-09-08T15:18:00
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_922
Metadata record format is ISO19139 XML (MD_Metadata)