Study of Intelligent Diagnosis System for Photoelectric Tracking Devices Based on Multiple Knowledge Representation

Abstract:

Aiming at the fatal flaws of the traditional diagnosis methods for the large-scale photoelectric tracking devices, such as poor stability and adaptive capacity, lack of inspiration and narrow domain knowledge of expert system, etc, more importantly, fundamentally improve the diagnostic efficiency and universality, in this paper, an intelligent mixed inference diagnosis expert system based on multiple knowledge representation and BP neural network is put forward. Firstly, some related key basic concepts and principles of intelligent fault diagnosis technology and several major applied diagnosis knowledge representation methods such as diagnosis fault tree, frame representation production rule and so on, were elaborated. Secondly, in view of high concurrency and relevancy of the system faults, a mixed reasoning mechanism combining BPNN and ES was researched. Finally, some interrelated essential implementation techniques, such as system architecture and VR technology, were also presented. Actual applications and experiments demonstrate that the proposed approach is robust and effective.

• Periodical: Advanced Materials Research (Volumes 179-180)
• Pages: 602-607
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.179-180.602
• Online since: January 2011
• Authors: Ming Liang Hou, Yu Ran Liu, Shu Bin Xing, Li Yun Su
• Keywords: BP Neural Network (BPNN), Fault Tree, Intelligent Diagnosis ES, OpenGL, Photoelectric Tracking

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