NDLI: Research on the detection information intelligent fusion of oil equipment

Content Provider	IEEE Xplore Digital Library
Author	Yudi Zhou Qingzhong Zhou
Copyright Year	2011
Description	Author affiliation: Department of POL Management Engineering, Logistics Engineering University, Chong Qing, China (Qingzhong Zhou) \|\| Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China (Yudi Zhou)
Abstract	By analyzing the character of oil equipment detection, the intelligent fusion model of detection information of oil equipment has been established. The feature-level fusion algorithm based on fuzzy neural network and expert system has been proposed, in which the expert system has been embedded into fuzzy neural network so that it could choose the membership function and adjust the network structure. The improved PSO algorithm has been adopted to train fuzzy neural network and prune fuzzy rules. Evidence theory has been applied to achieve the decision-making level fusion. Then, the results of feature-level fusion have been taken as the evidences to construct the frame of discernment. On the basis of the generalized evidence combination rule, the conflict evidence combination rule based on the weighted averaging method is proposed, and the prior knowledge in expert system has been utilized to adjust the evidence weights. The research results show that the process of detection information fusion has abilities of adapting and self-learning. This research has significant importance on reliability of improving oil equipment.
Starting Page	1170
Ending Page	1174
File Size	288972
Page Count	5
File Format	PDF
ISBN	9781612847191
e-ISBN	9781612847221
DOI	10.1109/MEC.2011.6025675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Decision making Expert system Information fusion Oil equipment detection Particle swarm optimization Training Fuzzy control Evidence theory Fuzzy neural networks Feature extraction Fuzzy neural network Expert systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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