REFERENCES

1. Liu D, Peng L, Zhao Z. A review of intelligent methods of health assessment technology. Intell Robot 2023;3:355-73.

2. Zhang B, Sun X, Liu S, Lv M, Deng X. Event-triggered adaptive fault-tolerant synchronization tracking control for multiple 6-DOF fixed-wing UAVs. IEEE Trans Vehi Technol 2021;71:148-61.

3. Petritoli E, Leccese F, Ciani L. Reliability and maintenance analysis of unmanned aerial vehicles. Sensors 2018;18:3171.

4. Zhen Z, Chen Y, Wen L, Han B. An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment. Aerosp Sci Technol 2020;100:105826.

5. Wang C, Lu N, Cheng Y, Jiang B. A telemetry data based diagnostic health monitoring strategy for in-orbit spacecrafts with component degradation. Adv Mech Eng 2019;11:1-14.

6. Chen J, Zhao Y, Xue X, Chen R, Wu Y. Data-driven health assessment in a flight control system under uncertain conditions. Appl Sci 2021;11:10107.

7. Wang B, Liu D, Peng Y, Peng X. Multivariate regression-based fault detection and recovery of UAV flight data. IEEE Trans Instrum Meas 2019;69:3527-37.

8. Li C, Li S, Zhang A, et al. A Siamese hybrid neural network framework for few-shot fault diagnosis of fixed-wing unmanned aerial vehicles. J Comput Des Eng 2022;9:1511-24.

9. Garcia DF, Perez AE, Moncayo H, et al. Spacecraft heath monitoring using a biomimetic fault diagnosis scheme. J Aerosp Inf Syst 2018;15:396-413.

10. Cui A, Zhang Y, Zhang P, Dong W, Wang C. Intelligent health management of fixed-wing UAVs: a deep-learning-based approach. In: 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV); 2020. pp. 1055-60.

11. Guo K, Liu L, Shi S, Liu D, Peng X. UAV sensor fault detection using a classifier without negative samples: a local density regulated optimization algorithm. Sensors 2019;19:771.

12. Shibl MM, Ismail LS, Massoud AM. A machine learning-based battery management system for state-of-charge prediction and state-of-health estimation for unmanned aerial vehicles. J Energy Storage 2023;66:107380.

13. Al-Haddad LA, Jaber AA, Al-Haddad SA, Al-Muslim YM. Fault diagnosis of actuator damage in UAVs using embedded recorded data and stacked machine learning models. J Supercomputing 2023;80:3005-24.

14. Zhang Z, Zhang M, Li G, Qin S, Xu C. ATSUKF-based actuator health assessment method for quad-copter unmanned aerial vehicles. Drones 2023;7:12.

15. Zhao Z, Quan Q, Cai KY. A health evaluation method of multicopters modeled by Stochastic Hybrid System. Aerosp Sci Technolo 2017;68:149-62.

16. Zhiyao Z, Peng Y, Xiaoyi W, et al. Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree. Chinese J Aeronaut 2019;32:444-53.

17. Yang Z, Ma J, Ji R, Yang B, Fan X. IAR-STSCKF-based fault diagnosis and reconstruction for spacecraft attitude control systems. IEEE Trans Instrum Meas 2022;71:3526112.

18. Ducard G, Geering HP. Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles. J Guid Control Dynam 2008;31:22-37.

19. Kressel I, Dorfman B, Botsev Y, et al. Flight validation of an embedded structural health monitoring system for an unmanned aerial vehicle. Smart Mater Struct 2015;24:075022.

20. Nassar B, Hussein W, Medhat M. Supervised learning algorithms for spacecraft attitude determination and control system health monitoring. IEEE Aerosp Electron Syst Mag 2017;32:26-39.

21. Sierra G, Orchard M, Goebel K, Kulkarni C. Battery health management for small-size rotary-wing electric unmanned aerial vehicles: an efficient approach for constrained computing platforms. Reliab Eng Syst Safety 2019;182:166-78.

22. Zhang W, Liu J, Gao M, Pan C, Huusom JK. A fault early warning method for auxiliary equipment based on multivariate state estimation technique and sliding window similarity. Comput Ind 2019;107:67-80.

23. Lopez L. Advanced electronic prognostics through system telemetry and pattern recognition methods. Microelectron Reliab 2007;47:1865-73.

24. Wang Z, Liu C. Wind turbine condition monitoring based on a novel multivariate state estimation technique. Measurement 2021;168:108388.

25. Liu HC, Liu L, Li P. Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator. Int J Syst Sci 2014;45:2012-30.

26. Jiang W, Wang M, Deng X, Gou L. Fault diagnosis based on TOPSIS method with Manhattan distance. Adv Mech Eng 2019;11:1687814019833279.

27. Li Y, Dai W, Zhu L, Zhao B. A novel fault early warning method for mechanical equipment based on improved MSET and CCPR. Measurement 2023;218:113224.

28. Yu K, Lin TR, Ma H, Li X, Li X. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning. Mech Syst Signal Proces 2021;146:107043.

29. Wu X, Kumar V, Ross Quinlan J, et al. Top 10 algorithms in data mining. Knowledge Inf Syst 2008;14:1-37.

30. Mohammadi M, Kavousi-Fard A, Dabbaghjamanesh M, Farughian A, Khosravi A. Effective management of energy internet in renewable hybrid microgrids: a secured data driven resilient architecture. IEEE Trans Ind Inform 2022;18:1896-904.

31. Tavana M, Soltanifar M, Santos-Arteaga FJ. Analytical hierarchy process: revolution and evolution. An Oper Res 2023;326:879-907.

32. Chu P, Liu JKH. Note on consistency ratio. Math Comput Model 2002;35:1077-80.

Complex Engineering Systems
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