Author(s)

Wenqian Song ¹, Yichuan Hao ²

Affiliation(s)

¹ Academy of Aerospace and Aviation, Tianjin University, Tianjin, 300072, China
² Airbus Tianjin Final Assembly Company Limited, Tianjin, 300300, China

Corresponding Author

Wenqian Song

ABSTRACT

With the complexity of aircraft systems, fault diagnosis was getting more and more difficult. The combination of different methods achieved improvement and becomes a tendency of research. Since rough set theory can effectively simplify information, combine rough set theory with neural networks, use the method of the improved attribute reduction algorithm which based on discernibility matrix to simplify the input information. Then improve the convergence of the network and efficiency of the whole data fusion system. The effectiveness of this method was verified by aircraft fault diagnosis test.

KEYWORDS

Fault diagnosis; aircraft system; rough set; neural network

CITE THIS PAPER

Wenqian Song, Yichuan Hao, Application of rough set-neural networks in civil aviation aircraft fault data processing. Journal of Electronics and Information Science (2022) Vol. 7: 9-14. DOI: http://dx.doi.org/10.23977/jeis.2022.070402.

REFERENCES

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[2] J. C. Patra, A. C. Kot and G. Panda, "An intelligent pressure using neural networks," IEEE Trans. Instrum. Meas., vol. 49, pp. 829-834, August 2000. 
[3] GAO J. Artificial neural network theory and simulation [M]. Beijing: Mechanical Industry Press, 2007.
[4] Zhang Y, Zhang G L. Based on rough set and neural network fault diagnosis [J]. Machinery and Electronics, 29: 116-117. 
[5] Liu, S. H., et al., "Research on efficient algorithms for rough set methods", Chin. J. Comput., 2003, 26, (5), pp. 524-529. 
[6] Wang Guoyin, Yu Hong and Yang Dachun, "Decisin Table Reduction based on Conditional Information Entropy", Chinese J. Computers, July 2002, 25(7), pp. 759-766.

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