Open AccessHealth status prediction of airborne systems based on transfer learning
Author(s) -
Qinhao Sun,
Dong Song,
Bin Lin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1654/1/012056
Subject(s) - context (archaeology) , computer science , transfer of learning , state (computer science) , mode (computer interface) , artificial intelligence , machine learning , transfer function , engineering , algorithm , geography , archaeology , operating system , electrical engineering
In this paper, based on the prediction of the decay mode of the system health state, a health pattern recognition and prediction method based on transfer learning is proposed. In the context of big data, the system's healthy decline mode is summarized from the massive historical flight data, and then the research on the health status of the airborne system based on the recognition results is carried out. Firstly, this paper demonstrates the feasibility of transfer learning applied to the prediction of the health status of airborne systems. Then, a HMM-based parameter migration health state prediction method is proposed. Finally, the model is verified by the hydraulic system of a certain type of aircraft. The results show that the model can predict the time when the health state changes.