AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (2.6 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Publishing Language: Chinese

An inversion method for extreme value of structural equivalent fatigue load of aircraft based on historical maintenance data

Qiushi XIA1Lei HUANG1Xiaobo ZHANG2Yinghui ZUO1Wenzhao WANG3Honglun XIE1Kuo TIAN1,4( )
State Key Laboratory of Structural, Analysis Optimization and CAE software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China
China Southern Technic Shenyang Base, Shenyang 110169, China
COMAC Shanghai Aircraft Design and Research Institute, Shanghai 201210, China
Liaoning Provincial Key Laboratory of Digital Twins for Structural Strength of Aircraft, Shenyang 110035, China
Show Author Information

Abstract

Timely and effective maintenance of aircraft structures is essential for ensuring operational safety, reducing operating costs, and extending service life. However, in current engineering practice, aircraft structural maintenance is often accompanied by a lack of complete and reliable load data, which leads to inaccurate strength and fatigue assessments of in-service aircraft structures and severely restricts subsequent structural modification and redesign. To address this inverse problem of load prediction, this paper proposes an inversion method for the extreme value of aircraft structural equivalent fatigue load based on historical maintenance data. First, historical maintenance records of aircraft structures are statistically analyzed, and the statistical fatigue life is determined by incorporating confidence and reliability assessment methods. Then, a numerical simulation model of the aircraft structure is established to obtain the predicted fatigue life under given loading conditions. Finally, an optimization framework is constructed in which the absolute difference between the predicted fatigue life and the statistical fatigue life is minimized, with the extreme value of the structural equivalent fatigue load treated as the design variable. Through iterative optimization, the structural equivalent fatigue load extreme value that best matches the actual service loading condition is identified. To verify the effectiveness of the proposed method, an aircraft kicker plate angle is selected as a case study, and the inversion results are compared with experimental data obtained from component-level fatigue tests. The results show that the prediction error of the proposed method is within 10%, demonstrating higher accuracy than conventional life prediction approaches based directly on strain data. These results indicate that the proposed method enables accurate inversion of the extreme value of aircraft structural equivalent fatigue load and provides useful guidance for aircraft structural modification design.

CLC number: V214.4;V414.4 Document code: A Article ID: 1000-6893(2026)14-233034-12

References

【1】
【1】
 
 
Acta Aeronautica et Astronautica Sinica

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
XIA Q, HUANG L, ZHANG X, et al. An inversion method for extreme value of structural equivalent fatigue load of aircraft based on historical maintenance data. Acta Aeronautica et Astronautica Sinica, 2026, 47(14). https://doi.org/10.7527/S1000-6893.2025.33034

1

Views

0

Downloads

0

Crossref

0

Scopus

0

CSCD

Received: 03 November 2025
Revised: 28 November 2025
Accepted: 24 December 2025
Published: 12 January 2026
© 2026 The Journal of Acta Aeronautica et Astronautica Sinica