Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Research on health degradation modeling of fixed wing UAVs
| DOI: | 10.11809/bqzbgcxb2023.09.009 |
| Keywords: | FW UAVs; health degradation modeling; SVDD; KDE; LSTM |
| Abstract: | The health status of Fixed wing UAVs will gradually deteriorate with its operation time. The research on its health degradation modeling method can be used to predict the remaining life of UAV, guide the advance maintenance and intelligent maintenance, thus effectively extend the service life of equipmentand reduce the probability of failure. In this paper, a modeling method based on multi task batch healthy flight data is proposed, which is used to solve the problem that the life cycle data of Fixed wing UAVs is difficult to obtain and the scarcity of fault data. This method is based on the Support Vector Data Description (SVDD) algorithm, and it constructs a health hypersphere model of UAV, characterizes the degree of health degradation with the trend of spherical distance offset, introduces the Kernel Density Estimation (KDE) algorithm to construct the overall health degradation index of UAV, and further uses the Long Short Term Memory (LSTM) algorithm to achieve the fitting of multi batch degradation trends. The method is expected to be applied to the maintenance and repair of Fixed wing UAVs and reduce the maintenance cost. |
| Issue: | Vol. 44 No. 9 (2023) |
| Published: | 2023-09-28 |
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