Big Data Empowering Civil Aircraft Health Management: A Full-Cycle Perspective

Civil aircraft that have obtained airworthiness certification—operating with complex structures under harsh service environments—are prone to abnormal states and potential failures. Aircraft health management, as a comprehensive integration of advanced technologies, embodies the overall engineering capability of civil aviation. The advent of big data has introduced new opportunities and challenges, driving the development of intelligent health management across the entire life cycle—from predictive strategies and real-time monitoring to anomaly detection and adaptive decision support. This paper reviews current applications and technological trends in big data-driven health management for all airworthiness-certified civil aviation aircraft, with a focus on real-time fault diagnosis, Remaining Useful Life (RUL) prediction, large-scale fault data analytics, and emerging approaches enabled by generative models. The analysis highlights the role, necessity, and future directions of these technologies in advancing sustainable and intelligent civil aviation.

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Publication Details

Journal
Aerospace
Published
2025-12-26
DOI
https://doi.org/10.3390/aerospace13010024
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Big Data Empowering Civil Aircraft Health Management: A Full-Cycle Perspective

Yaogang Wu, Chao Ma, Jianxin Xu, Donglei Sun et al.
Aerospace
Machine Fault Diagnosis Techniques
article

Big Data Empowering Civil Aircraft Health Management: A Full-Cycle Perspective

Yaogang Wu, Chao Ma, Jianxin Xu, Donglei Sun, Zhengbo Gu, Xiang Ba
article en

Abstract

Civil aircraft that have obtained airworthiness certification—operating with complex structures under harsh service environments—are prone to abnormal states and potential failures. Aircraft health management, as a comprehensive integration of advanced technologies, embodies the overall engineering capability of civil aviation. The advent of big data has introduced new opportunities and challenges, driving the development of intelligent health management across the entire life cycle—from predictive strategies and real-time monitoring to anomaly detection and adaptive decision support. This paper reviews current applications and technological trends in big data-driven health management for all airworthiness-certified civil aviation aircraft, with a focus on real-time fault diagnosis, Remaining Useful Life (RUL) prediction, large-scale fault data analytics, and emerging approaches enabled by generative models. The analysis highlights the role, necessity, and future directions of these technologies in advancing sustainable and intelligent civil aviation.

AerospaceVol. 13(1)
University of Nottingham Ningbo China (CN), Civil Aviation University of China (CN), Southern University of Science and Technology (CN)
Natural Science Foundation of Tianjin City
Responsible consumption and production
Openalex Percentile: Top 36%
Machine Fault Diagnosis Techniques
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Big Data Empowering Civil Aircraft Health Management: A Full-Cycle Perspective — Yaogang Wu, Chao Ma, et al. · Aerospace (2025) | TGRS Research Map | TGRS