Data mining for prediction of aircraft component replacement.

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Proceedings titleSpecial Issue on Data Mining
ConferenceIEEE Intelligent Systems Jr, December 1999.
Subjectdata mining; machine learning; aircraft health monitoring; component failure prediction; apprentissage automatique; surveillance de l'état de l'aéronef; prédiction des pannes
AbstractThe operation and maintenance of modern sensor-equipped systems such as passenger aircraft generate vast amounts of numerical and symbolic data. Learning models from this data to predict problems with component may lead to considerable saving, reducing the number of delays, and increasing the overall level of safety. Several data mining techniques exist to learn models from vast amount of data. However, the use of these techniques to infer the desired models from the data obtained during the operation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include: data gathering, data labeling, data and model integration, and model evaluation. This paper presents an approach that addresses these issues. We also report results from the application of this approach to build models that predict problems for a variety of aircraft components.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number43616
NPARC number5763624
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Record identifier6492eae7-6bf5-424d-af18-f6957aec61c3
Record created2009-03-29
Record modified2016-05-09
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