DocumentCode :
3007944
Title :
Application of fuzzy neural network in fault diagnosis for scraper conveyor vibration
Author :
Xiaofeng Gong ; Xianmin Ma ; Yongqiang Zhang ; Jianxiang Yang
Author_Institution :
Coll. of Electr. & Control Eng., Xi´an Univ. of Sci. & Technol., Xi´an, China
fYear :
2013
fDate :
26-28 Aug. 2013
Firstpage :
1135
Lastpage :
1140
Abstract :
In order to avoid losses, which is caused by electromechanical failure in the coal large transport equipments, this article introduced a model that integrated by breakdown extraction, failure diagnosis and fault analysis in the large-scale scraper chain conveyor breakdown. The weak signal of mechanical vibration detection by ANFIS is adopted. And it is also used for Troubleshooting effectively and accurately. Lastly, an effective diagnosis suggestion is given through the instrumentation KS-2000. And by this way, we not only proved the feasibility and superiority of this plan, but also achieved predictive maintenance in the true sense.
Keywords :
conveyors; failure analysis; fault diagnosis; fuzzy neural nets; vibrations; ANFIS; breakdown extraction; coal large transport equipments; electromechanical failure; fault analysis; fault diagnosis; fuzzy neural network; instrumentation KS-2000; large scale scraper chain conveyor breakdown; mechanical vibration detection; predictive maintenance; scraper conveyor vibration; weak signal; Electric breakdown; Fault diagnosis; Monitoring; Rotors; Training; Vibration measurement; Vibrations; ANFIS; coal scraper conveyor; fault diagnosis; vibration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
Type :
conf
DOI :
10.1109/ICInfA.2013.6720466
Filename :
6720466
Link To Document :
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