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Method of fault prediction for avionics components based on stacking regression

Published online by Cambridge University Press:  23 December 2024

W.H. Li ,
G. Li Open the ORCID record for G. Li [Opens in a new window] ,
Y. Liu ,
J.T. Ma ,
Z.D. Wu ,
X. Tang ,
W.C. Sun  and
T.Z. Wen
W.H. Li
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
G. Li*
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China Repair Brigade, 77120 Unit of the Chinese People’s Liberation Army, Chengdu, China
Y. Liu
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
J.T. Ma
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
Z.D. Wu
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
X. Tang
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
W.C. Sun
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
T.Z. Wen
Affiliation:
Aviation Combat Service Academy, Naval Aviation University, Yantai, China
*
Corresponding author: G. Li; Email: 504123921@qq.com
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Abstract

As avionics systems become increasingly complex, traditional fault prediction methods are no longer sufficient to meet modern demands. This paper introduces four advanced fault prediction methods for avionics components, utilising a multi-step prediction strategy combined with a stacking regressor. By selecting various standard regression models as base regressors, these base regressors are first trained on the original data, and their predictions are subsequently used as input features for training a meta-regressor. Additionally, the Tree-structured Parzen Estimator (TPE) algorithm is employed for hyperparameter optimisation. The experimental results demonstrate that the proposed stacking regression methods exhibit superior accuracy in fault prediction compared to traditional single-model approaches.

Keywords

multi-step predictionavionics componentsmeta-regressorstacking regressionfault prediction
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1336 , June 2025 , pp. 1448 - 1467
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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