Aircraft interior failure pattern recognition utilizing text mining and neural networks

Authors:
Rogério S. Rodrigues;Pedro Paulo Balestrassi;Anderson P. Paiva;Alberto Garcia-Diaz;Fabricio J. Pontes
Affiliations:
Federal University of Itajuba, Itajuba, Brazil;Federal University of Itajuba, Itajuba, Brazil and University of Tennessee at Knoxville, Knoxville, USA 37919;Federal University of Itajuba, Itajuba, Brazil;University of Tennessee at Knoxville, Knoxville, USA 37919;Universidade Estadual Paulista (UNESP), Guaratinguetá, Brazil
Venue:
Journal of Intelligent Information Systems
Year:
2012

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Abstract

Being more competitive is routine in the aeronautical sector. Airline competitiveness is affected by such factors as time, price, reliability, availability, safety, technology, quality, and information management. To remain competitive, airlines must promptly identify and correct failures found in their fleet. This study aims at reducing the time spent on identifying and correcting such failures logged. Utilizing Text Mining techniques during the pre-processing phase, our study processes an extensive database of events from commercial regional jets. The result is a unique list of keywords that describes each reported failure. Later, an Artificial Neural Network (ANN) identifies and classifies failure patterns, yielding a respective disposition for a given failure pattern. Approximately five years of historical data was used to build and validate the present model. Results obtained were promising.