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An On-Board Shock Absorber Detection Method for General Aviation Aircraft Landing Gears
Chunsheng Li1, Haoyu Li1, Zongguang Shen1
1Department of Aircraft Engineering, College of Aviation Engineering, Civil Aviation Flight University of China, Chengdu 641419, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces an on-board method using deep learning to detect shock absorber leaks in general aviation aircraft. The system achieves over 95% precision, enhancing safety and maintenance.
Area of Science:
- Aerospace Engineering
- Mechanical Engineering
- Artificial Intelligence
Background:
- Shock absorber leaks in general aviation aircraft pose safety risks.
- Current detection methods may lack efficiency and real-time capabilities.
Purpose of the Study:
- Develop an on-board, sensor-efficient method for detecting shock absorber leaks.
- Analyze the impact of gas and oleo leakage on landing gear dynamics.
Main Methods:
- Created a rigid-flexible coupled multi-body system (MBS) model of landing gear.
- Developed a database of common leakage faults and landing conditions.
- Trained and tested a deep learning model using simulated data.
Main Results:
- The deep learning model achieved over 95% precision in detecting shock absorber leaks.
- The method accurately simulates the effects of gas and oleo leakage on air spring force.
Conclusions:
- The proposed deep learning method offers a practical solution for real-time health monitoring of landing gear shock absorbers.
- This contributes to improved maintenance strategies and operational safety in general aviation.
- Further validation through drop tests or flight tests is recommended.
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