Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

In a prior study, multiple regression techniques were applied to Flight Operation Quality Assurance-derived data to develop parsimonious models for fuel consumption on the Boeing 757 aircraft. This study applied several data mining algorithms, including neural networks, to the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general, data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree models and Multilayer Perception neural networks reported correlation coefficients of about .99. These data mining models show great potential for use in further examining large FOQA databases for operational and safety improvements.

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 6-24
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01055288
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 10 2007 1:47PM