Title :
Failure diagnosis system of continuous miner cutting system based on hybrid optimization neural network
Author :
Li Xiaohuo ; Zhao Yingbo ; Zhang Jinghui ; Liu Zhisen
Author_Institution :
Coll. of Mech. Eng., Liaoning Tech. Univ., Fuxin, China
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.
Keywords :
cutting; failure analysis; fault diagnosis; genetic algorithms; mechanical engineering computing; mining; mining equipment; neural nets; particle swarm optimisation; GA-PSO hybrid optimization; MATLAB7.1; SQL SERVER 2000; VC6.0; continuous miner cutting system; failure diagnosis system; failure rate; fuzzy neural network; hybrid optimization neural network; intelligent fault diagnosis expert system; maintenance time; man-machine conversation function; man-machine interface; programming platform; Artificial neural networks; Databases; Expert systems; Fault diagnosis; Genetic algorithms; Optimization; GA-PSO; continuous miner; cutting system; fault diagnosis; neural network;
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
DOI :
10.1109/ICEICE.2011.5777223
