Aircraft interior failure pattern recognition utilizing text mining and neural networks
Nenhuma Miniatura disponível
Data
2012-06-01
Autores
Rodrigues, Rogerio S.
Balestrassi, Pedro Paulo
Paiva, Anderson P.
Garcia-Diaz, Alberto
Pontes, Fabricio J. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Springer
Resumo
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.
Descrição
Palavras-chave
Artificial Neural Network (ANN), Text mining, Failure pattern, Aircraft log book, Repair
Como citar
Journal of Intelligent Information Systems. Dordrecht: Springer, v. 38, n. 3, p. 741-766, 2012.