NDLI: Failure diagnosis system of continuous miner cutting system based on hybrid optimization neural network

Content Provider	IEEE Xplore Digital Library
Author	Li Xiaohuo Zhao Yingbo Zhang Jinghui Liu Zhisen
Copyright Year	2011
Description	Author affiliation: College of Mechanical Engineering, Liaoning Technical University, 123000, Fuxin City, China (Li Xiaohuo; Zhao Yingbo; Zhang Jinghui; Liu Zhisen)
Abstract	In order to diagnose faults of a continuous miner cutting system quickly and effectively, the GA-PSO hybrid optimization method of fuzzy neural network is used in fault diagnosis in the paper, a intelligent fault diagnosis expert system of a continuous miner cutting system is designed by means of taking VC6.0 as the programming platform, using SQL SERVER 2000 as database, embedding MATLAB7.1 in the internal. The system is simple in man-machine interface and good in man-machine conversation function, capable of analyzing accurately and judging properly failures of a continuous miner cutting system. The research has some guidance for understanding the system state, reducing the failure rate, saving maintenance time and improving the reliability of continuous miner's work and productivity, enhancing the performance of a continuous miner.
Starting Page	403
Ending Page	406
File Size	631500
Page Count	4
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5777223
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Continuous miner Databases Artificial neural networks GA-PSO Neural network Expert systems Optimization Cutting system Genetic algorithms
Content Type	Text
Resource Type	Article

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