Research on Key Technologies for Failure Prediction and Health Management of Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in the modern national economy, with a wide range of applications covering a variety of fields such as agriculture, industry, transport, environmental monitoring, medical rescue, and so on, and have become a new driving force for economic development. The significance of fault prediction and health management can improve the reliability of equipment, and fault diagnosis technology can accurately find out the cause and location of equipment failure, so as to repair and restore the original function of the equipment in time. This helps to ensure the stable operation of the equipment and improve its reliability of the equipment. At the same time, it provides customized maintenance recommendations and the best time to repair the equipment, minimizing unnecessary maintenance costs. Plan maintenance ahead of time: Health prediction technology can assess the health of your equipment and predict performance degradation trends and remaining useful life. How to quickly diagnose the cause of UAV failure, accurately determine the health status, combined with the current UAV maintenance guarantee capacity, put forward rationalised maintenance countermeasure suggestions, to ensure that the UAV warranty service not only meets the economic demand of cost control, but also can efficiently support the mission requirements of its execution, so the research has a very realistic economic value and practical significance.

Authors

Publication Details

Journal
Engineering Advances
Published
2025-05-14
DOI
https://doi.org/10.26855/ea.2025.04.003
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Key Technologies for Failure Prediction and Health Management of Unmanned Aerial Vehicles

Kai Han, Lu Wang, Y. H. Yan
Engineering Advances
Advanced Sensor and Control Systems
article

Research on Key Technologies for Failure Prediction and Health Management of Unmanned Aerial Vehicles

Kai Han, Lu Wang, Y. H. Yan
article en

Abstract

Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in the modern national economy, with a wide range of applications covering a variety of fields such as agriculture, industry, transport, environmental monitoring, medical rescue, and so on, and have become a new driving force for economic development. The significance of fault prediction and health management can improve the reliability of equipment, and fault diagnosis technology can accurately find out the cause and location of equipment failure, so as to repair and restore the original function of the equipment in time. This helps to ensure the stable operation of the equipment and improve its reliability of the equipment. At the same time, it provides customized maintenance recommendations and the best time to repair the equipment, minimizing unnecessary maintenance costs. Plan maintenance ahead of time: Health prediction technology can assess the health of your equipment and predict performance degradation trends and remaining useful life. How to quickly diagnose the cause of UAV failure, accurately determine the health status, combined with the current UAV maintenance guarantee capacity, put forward rationalised maintenance countermeasure suggestions, to ensure that the UAV warranty service not only meets the economic demand of cost control, but also can efficiently support the mission requirements of its execution, so the research has a very realistic economic value and practical significance.

Engineering AdvancesVol. 5(2)
Openalex Percentile: Top 70%
Advanced Sensor and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on Key Technologies for Failure Prediction and Health Management of Unmanned Aerial Vehicles — Kai Han, Lu Wang, et al. · Engineering Advances (2025) | TGRS Research Map | TGRS