Abstract
In order to diagnose the unmanned helicopter rotor’s unbalance fault accurately, a method based on particle swarm optimization algorithm and generalized regression neural network (PSO-GRNN) is proposed. The average mean square error got from cross-validation is used as the fitness function of particle swarm, then the optimal GRNN smooth factor is attained by using particle swarm optimization algorithm, and an optimal model for fault diagnosis is achieved finally. It can be concluded that, based on the PSO-GRNN model, the type and the grade of the helicopter rotor’s unbalance can be diagnosed effectively, the diagnosis accurate rate of fault type is up to 94.29 % and the maximum error of fault grade is only 6.54 %, which is perfectly satisfied for the requirement of project.
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References
Pawar PM, Ganguli R (2007) Helicopter rotor health monitoring-a review. Proc Inst Mech Eng Part G J Aerosp Eng 221(5):631–647
Ganguli R, Chopra I, Haas DJ (1998) Helicopter rotor system fault detection using physics-based model and neural networks. AIAA J 36(6):1078–1086
Jianhang GYZZY (2002) Novel helicopter rotor imbalance fault diagnosis using only information from fuselage vibration. J Vibr Eng 4:004
Gao YD, Zhang ZC, Yu JH (2004) Novel method for diagnosis of helicopter rotor compound imbalance fault by using fuselage vibrations. Acta Aeronaut Astronaut Sin Ser A B 25(1):26–30 (in chinese)
Shi F, Wang XC et al (2010) MATLAB neural network analysis of 30 cases. University of Aeronautics and Astronautics Press, Beijing (in chinese)
Chen CY, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. In: 2004 IEEE international conference on networking, sensing and control, vol 2. IEEE, pp 789–794
Zengchang GYZ (2003) A theoretical basis for diagnosis of helicopter rotor imbalance fault using only information from fuselage vibrations. J Vibr Meas Diagn 4:010 (in chinese)
Zengchang GYZ (2009) Experimental study on unbalance fault diagnosis of helicopter rotor. J Vibr Meas Diagn 2:022 (in chinese)
Dun S-P (2010) Research on helicopter rotor imbalance fault experiment and diagnosis method. Nanjing University of Aeronautics and Astronautics, Nanjing (in chinese)
Yao F-H (2011) Research on helicopter rotor fault diagnosis based on blind source separation and artificial immune system. Nanjing University of Aeronautics and Astronautics, Nanjing (in chinese)
Acknowledgements
This work was supported by the National Science and Technology Supporting Plan of China under Grant 2014BAD06B07, and supported by the Transformation and Industrialization of Scientific and Technological Achievements Program of Hunan Province, China, under Grant 2012CK1003.
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Xie, Xh., Xu, L., Zhou, L., Tan, Y. (2016). GRNN Model for Fault Diagnosis of Unmanned Helicopter Rotor’s Unbalance. In: Huang, B., Yao, Y. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Automatic Control. Lecture Notes in Electrical Engineering, vol 367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48768-6_61
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DOI: https://doi.org/10.1007/978-3-662-48768-6_61
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