A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

Author:

Cui Jian Guo1,Wu Can1,Jiang Li Ying1,Qi Yi Wen1,Li Guo Qiang2

Affiliation:

1. Shenyang Aerospace University

2. Shenyang Aircraft Design and Research Institute

Abstract

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

Publisher

Trans Tech Publications, Ltd.

Reference5 articles.

1. Tian Chen, Jianguo Sun. Aeroengine Gas Path Fault Diagnosis Using Rough Sets and Neural Networks[J], Journal of Aerospace Power, Vol. 207-212 (2006), p.21.

2. Yanji Jiang. Research on Key Technologies of Multi-sensor Data Fusion[D]. Harbin: Harbin Engineering University, (2010).

3. Hue C, Cadre J PL, Perez P. Sequential Monte Carlo methods for multitarget tracking and data fusion. IEEE on Signal Processing, Vol. 309-325 (2002), p.50.

4. Yili Zhai, Yisong Dai. Study of Adaptive Weighted Fusion Estimated{TTP}-8189 Algorithm of Multisensor Data [J], Acta Metrologica Sinica, Vol. 69-75 (1998), p.19.

5. Lu,P.J., Zhang, M.C., Ganguli, R., etal. An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks, Proceedings of ASME TURBO EXPO 2000, May 8-11, 2000, Munich, Germany.

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