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Mechanical Dispatch Reliability Prediction for Civil Aircraft Considering Operational Parameters

Yunwen Feng1, Zhicen Song1,*, Cheng Lu2
1 School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China
2 Department of Aeronautics and Astronautics, Fudan University, Shanghai, 200433, China
* Corresponding Author: Zhicen Song. Email:
(This article belongs to this Special Issue: Computer-Aided Structural Integrity and Safety Assessment)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1925-1942. https://doi.org/10.32604/cmes.2022.022680

Received 21 March 2022; Accepted 10 May 2022; Issue published 20 September 2022

Abstract

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 the most 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 of MDR, 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 efficient method for the MDR 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.

Keywords

Mechanical dispatch reliability; GRA-RBF; civil aircraft; artificial neural network

Cite This Article

Feng, Y., Song, Z., Lu, C. (2023). Mechanical Dispatch Reliability Prediction for Civil Aircraft Considering Operational Parameters. CMES-Computer Modeling in Engineering & Sciences, 134(3), 1925–1942.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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