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Proceedings Paper

Application research of fault diagnosis expert system for photoelectric tracking device based on BP NN
Author(s): Mingliang Hou; Yong Zhang; Feng Liu; Jian Zhang; Liyun Su
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Paper Abstract

In order to overcome the deficiencies of poor adaptive capacity, lack of inspiration and narrow domain knowledge of expert system and fundamentally improve the diagnostic efficiency, an intelligent fault diagnosis expert system for photoelectric tracking devices, based on BP neural network, is put forward. Firstly, in this paper, some key basic concepts and principles of intelligent fault diagnosis technology are proposed. Secondly, according to the difficulty of multiple and coupling fault diagnosis, after making a comparative analysis of the related BP neural network algorithms, such as the quasi-Newton method, the stretch BP method and the conjugate gradient method, a neural network fault diagnosis reasoning method based on the Levenberg-Marquardt is designed, which combined the implementation of the diagnosis expert system. Finally, several interrelated essential implementation issues, such as the architecture of the system and the VR technology based on OpenGL, are also discussed. Practical experiments and applications demonstrate that the proposed approach is effective, robust and universal.

Paper Details

Date Published: 22 October 2010
PDF: 6 pages
Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 76584E (22 October 2010); doi: 10.1117/12.865714
Show Author Affiliations
Mingliang Hou, Huaihai Institute of Technology (China)
Yong Zhang, Huaihai Institute of Technology (China)
Feng Liu, Shandong Shengli Vocational College (China)
Jian Zhang, Huaihai Institute of Technology (China)
Liyun Su, Chongqing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7658:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology
Yadong Jiang; Bernard Kippelen; Junsheng Yu, Editor(s)

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