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

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 ta...

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Bibliographic Details
Main Authors: Li Xiaohuo, Zhao Yingbo, Zhang Jinghui, Liu Zhisen
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
continuous miner
cutting system
Expert systems
Fault diagnosis
GA-PSO
Genetic algorithms
neural network
Optimization
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Summary: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.
DOI:10.1109/ICEICE.2011.5777223