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2012 | 3 | nr 2 | 21--26
Tytuł artykułu

Aircraft Engine Overhaul Demand Forecasting Using ANN

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Due to the unpredictable nature for aircraft maintenance repair parts demand, MRO (Maintenance, Repair, Overhaul) business perceive difficulties in forecasting and are currently looking for a superior forecasting solution. This paper deals with techniques applicable to predicting spare part demand replacement during helicopter PZL 10W engine overhaul - operating according to hard - time. The experimental results show new forecasting method based on hard - time as the predicted time of required demand and ANN technique as forecasting models predicted numbers of spare parts. The evolution for a new forecasting method, which will be a predictive error-forecasting model which compares and evaluates forecasting methods, based on their factor levels when faced with intermittent demand show as possibility of big changes in MRO lean manufacturing. The results confirm the continued superiority of the new method, whereas, most commonly leveraged methods such as moving average used by MRO business are found to be questionable, and consistently producing poor forecasting performance.(original abstract)
Rocznik
Tom
3
Numer
Strony
21--26
Opis fizyczny
Twórcy
autor
  • WSK "PZL Rzeszów" S.A.
  • Rzeszów University of Technolog, Poland
Bibliografia
  • Nazih A., Servis parts management, Demand forecasting and Inventory Control, Springer- Verlag, Londyn 2011.
  • Ghobbar A.A., Friend C.H., Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model, Computers & Operation research, pp. 2097-2114, 2003.
  • Croston J.D.: Forecasting and stock control for intermittent demands, Operational Research, pp. 289-303, 1972.
  • Syntetos A.A., Boylan J.E., On the bias of intermittent demand estimates, International Journal Production Economics, 71, 457-466, 2001.
  • Syntetos A.A., Boylan J.E., The accuracy of intermittent demand estimates, International Journal Production Economics, 21, 303-314, 2005.
  • Boliński B., Stelmaszczyk Z., Aviation power plant. Turboprop engines exploitation, WKiL, Warszawa, 1981.
  • Kustroń K., Selected problems of helicopter operation, PBiETL, Warszawa, 2010.
  • Amin Naseri M.R., Tabar B.R., Neural network approach to lumpy demand forecasting for spare parts in process industries, Prooceedings of the international conference on computer and communication engineering, Kuala Lumpur, pp. 1378-1382, May, 2008.
  • Chen F.L., Chen Y.C., Kuo J.Y.: Applying moving back-propagation neural network and moving fuzzy neuron network to predict the requirement of critical spare parts, Expert Systems with Applications, Pergamon Press, NY, pp. 4358-4367, 2010.
  • Sęp J., Kozik P., Application of neural networks to improve efficiency planning overhaul, TIOPS, Regietów k/Gorlic, 2008.
  • Diao Y., Passin K.M., Fault diagnosis for a turbine engine, Control Engineering Practice, 12, 9, 1151-1165, 2004.
  • Volponi A.J., Brotherton T., Luppold R., Development of an Information Fusion System for Engine Diagnostics and Health Management, AIAA 1st Intelligent Systems Technical Conference, Chicago, Report A528624, 2004.
  • Bouraou N., Zazhitskiy O., Decision making about aircraft engine blades condition by using Neural Network at thesteady-state and non-steaty-state modes, Diagnostics, 3, 71-74, 2008.
  • Luka P., Application artificial intelligence in forecasting, StatSoft, Kraków, 2009.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171569231

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