Rough Set Fuzzy Neural Network Fault Diagnosis for the over Current Detection of Coal Mining Scraper

Yong Qiang Zhang; Xian Min Ma; Jian Xiang Yang; Yan Ni Zhang

doi:10.4028/www.scientific.net/AMM.321-324.2146

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Rough Set Fuzzy Neural Network Fault Diagnosis for...

Rough Set Fuzzy Neural Network Fault Diagnosis for the over Current Detection of Coal Mining Scraper

Abstract:

In this paper the rough set fuzzy neural network is used to monitor the over current fault problem of the mining scraper conveyor motor driving system. The phase current signals are input into the neural network, and then the current signals are processed with fuzzy logic set theory for optimization. Because too many rules may lead to complex computation, the rough set theory is used to reduce the rules after the signal characteristics are extracted. The simulation results show that the precision and reliability of motor driving system of the mining scraper conveyor can be improved by this method.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

2146-2151

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.2146

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Online since:

June 2013

Authors:

Yong Qiang Zhang, Xian Min Ma, Jian Xiang Yang, Yan Ni Zhang

Keywords:

Fault Diagnosis, Fuzzy Inference, Neural Network (NN), Over Current, Rough Set (RS)

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References

[1] Lou Shuntian Shi Yang. System analysis and design based on MATLAB neural network. Xi'an: Xi'an Electronic and Science University press, (1999)

Google Scholar

[2] Yu Heji, Chen Changzheng. Intelligent diagnosis based on neural network. Beijing: Metallurgical Industry Press

Google Scholar

[3] Tenbohlen S. Figel F.On-line condition monitoring of power transformers[C].Power Engineering Society Winter Meeting, IEEE, Vol: 3, 2000, Page(s): 2211-2216.

DOI: 10.1109/pesw.2000.847699

Google Scholar

[4] Birlasekaran, S.; Fetherston,F.Off/on-line FRA condition monitoring technique for power transformer[J].IEEE Power Engineering Review,Volume:19 Issue:8,Aug.1999 Page(s) 54-56

DOI: 10.1109/39.780991

Google Scholar

[5] Rao Hong is based on information fusion and Rough set theory in hydraulic machine fault diagnosis method based on [D]. doctoral thesis of Nanchang University.(2009)

Google Scholar

[6] Hu Wenping electrical equipment based on intelligence information fusion fault diagnosis new technology study of [D]. doctoral thesis of Huazhong University of Science and Technology.(2005)

Google Scholar

[7] Zhu Daqi, Yu Shenglin. Fault diagnosis method based on knowledge review, Journal of Anhui University of Technology, 2002,19 (3): 197-204

Google Scholar