DocumentCode :
2749160
Title :
Study on Ventilation System Reliability Early-Warning Based on RS-ANN
Author :
Wang, Hong-de ; Zhao, Yi
Author_Institution :
Sch. of Civil & Safety Eng., Dalian Jiaotong Univ., Dalian, China
Volume :
3
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
598
Lastpage :
602
Abstract :
System reliability early warning aims to measure the rate of tolerance deviation from the reliability index borderline of the system running states, to confirm the early warning grade, and to assist decision-making warning. Analyzed and compared the methods of system early warning from home and abroad, and combined Rough Set (RS) theory and Artificial Neuron Network (ANN) technique, a new method based on Rough Set and Artificial Neuron Network (RS-ANN) to solve ventilation system reliability early warning is put forward. Firstly, an index system which adapted to ANN analysis for mine ventilation system reliability early warning is established. Secondly, a prepositive system with RS method to optimize the index system of ANN is put forward. Thirdly, the simulation model for ventilation system reliability early warning which based on RS-ANN is set up. Finally, the effectiveness of this method is proved by an example. Computer simulation shows that the simulation result by RS-ANN methods is consistent with the result of ANN analysis, but the training efficiency increased 667 times.
Keywords :
alarm systems; coal; decision making; digital simulation; mining industry; neural nets; reliability theory; rough set theory; ventilation; RS-ANN; artificial neuron network technique; computer simulation; decision making warning; index system optimization; mine ventilation system; reliability index borderline; rough set theory; system reliability early warning; tolerance deviation rate; Computational modeling; Computer simulation; Decision making; Fuzzy systems; Neurons; Optimization methods; Reliability engineering; Reliability theory; Safety; Ventilation; Rough Set and Artificial Neuron Network (RS-ANN); index system; reliability early-warning; ventilation system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.809
Filename :
5359052
Link To Document :
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