Mechanical Dispatch Reliability Prediction for Civil Aircraft Considering Operational Parameters

To effectively predict the mechanical dispatch reliability(MDR),the artificial neural networks method combined with aircraft operation health status parameters is proposed,which introduces the real civil aircraft operation data for verification,to improve the modeling precision and computing efficiency.Grey relational analysis can identify the degree of correlation between aircraft system health status(such as the unscheduled maintenance event,unit report event,and services number)and dispatch release and screen out themost closely related systems to determine the set of input parameters required for the prediction model.The artificial neural network using radial basis function(RBF)as a kernel function,has the best applicability in the prediction of multidimensional,small sample problems.Health status parameters of related systems are used as the input to predict the changing trend ofMDR,under the artificial neural network modeling framework.The case study collects real operation data for a certain civil aircraft over the past five years to validate the performance of the model which meets the requirements of the application.The results show that the prediction quadratic error Ep of the model reaches 6.9×10−8.That is to say,in the existing operating environment,the prediction of the number of delay&cancel events per month can be less than once.The accuracy of RBF ANN,BP ANN and GA-BP ANN are compared further,and the results show that RBF ANN has better adaptability to such multidimensional small sample problems.The efforts of this paper provide a highly efficientmethod for theMDR prediction through aircraft system health state parameters,which is a promising model to enhance the prediction and controllability of the dispatch release,providing support for the construction of the civil aircraft operation system.

Data-driven reliability analysis of Boeing 787 Dreamliner

The Boeing 787 Dreamliner,launched in 2011,was presented as a game changer in air travel.With the aim of producing an efficient,mid-size,wide-body plane,Boeing initiated innovations in product and process design,supply chain operation,and risk management.Nevertheless,there were reliability issues from the start,and the plane was grounded by the U.S.Federal Aviation Administration(FAA)in 2013,due to safety problems associated with Li-ion battery fires.This paper chronicles events associated with the aircraft’s initial reliability challenges.The manufacturing,supply chain,and organizational factors that contributed to these problems are assessed based on FAA data.Recommendations and lessons learned are provided for the benefit of engineers and managers who will be engaged in future complex systems development.