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Point and interval prediction of aircraft engine maintenance cost by bootstrapped SVR and improved RFE

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Abstract

Maintenance cost of aircraft engine is an important component of aircraft operation cost. The prediction of aircraft engine maintenance cost can provide decision support for airline to make reasonable maintenance plan and maintenance fund management. Considering that the prediction accuracy of engine maintenance cost is not high in the case of small samples, this paper proposes a bootstrapped support vector regression (SVR) prediction method based on improved recursive feature elimination, which realizes the point and interval prediction of engine maintenance cost in aircraft operation phase. First, the recursive feature elimination (RFE) is improved and then combined with SVR to select feature subsets. Second, particle swarm optimization (PSO) algorithm is applied to optimize the improved RFE-SVR model (IRFE-SVR) parameters. Finally, the point and interval estimates are obtained by bootstrapped IRFE-SVR. To demonstrate the performance of the bootstrapped IRFE-SVR, experiments on UCI and a real case study of engine maintenance cost prediction are conducted. The results on UCI and real datasets show that the bootstrapped IRFE-SVR method has high accuracy and reliability.

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Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We appreciated Chaoming Hu for providing ideas for the experimental analysis of the manuscript.

Funding

This work is supported by The National Natural Science Foundation of China (Nos. 72271077, 71801071, 71922009, 72071056, 71871080, 71601065, 71690235, 71501058, 71601060), and Innovative Research Groups of the National Natural Science Foundation of China (71521001), Anhui Province Natural Science Foundation (No. 1908085MG223), the Fundamental Research Funds for the Central Universities (Nos. JZ2019HGTA0051, JZ2019HGBZ0131), Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project), the Project of Key Research Institute of Humanities and Social Science in University of Anhui Province, Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making (Hefei University of Technology), Ministry of Education.

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Junying Hu, Xiaofei Qian & Xinbao Liu

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making of the Ministry of Education, Hefei, 230009, People’s Republic of China

    Junying Hu, Xiaofei Qian & Xinbao Liu

  3. School of Economics, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Changchun Tan

  4. Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies, Hefei, 230009, People’s Republic of China

    Xiaofei Qian

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  1. Junying Hu
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JH and XQ wrote the main manuscript text, and all authors reviewed the manuscript.

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Correspondence to Xiaofei Qian.

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Hu, J., Qian, X., Tan, C. et al. Point and interval prediction of aircraft engine maintenance cost by bootstrapped SVR and improved RFE. J Supercomput 79, 7997–8025 (2023). https://doi.org/10.1007/s11227-022-04986-3

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