DocumentCode :
2269405
Title :
Classification and Recognition of Detecting Parameters for Cement Mill
Author :
Qian, Hui ; Wang, Xiaohong ; Yu, Hongliang
Author_Institution :
Sch. of Control Sci. & Eng., Univ. of Jinan, Jinan
Volume :
3
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
402
Lastpage :
406
Abstract :
In view of the complex milling process of cement raw material, aim at practical technological features of cement mill, all relative factors to process are measured and classified as faulty condition and normal condition. This article applies classification and recognition algorithm to detecting main parameters for cement mill, and then to judge the operating condition. In view of fault detect that bases on the signal to judge the trends of fault and give an alarm, the running patterns of system are built with an improved ART-2 cluster parsing algorithm under normal condition, carry on the correct recognition to the status of cement mill, in turn to take the right recognition of milling working condition.
Keywords :
ART neural nets; cement industry; fault diagnosis; milling; pattern clustering; pattern recognition; signal classification; signal detection; ART-2 cluster parsing algorithm; cement mill; faulty condition detection; milling process; signal classification algorithm; signal recognition algorithm; Classification algorithms; Clustering algorithms; Employee welfare; Fault detection; Fault diagnosis; Feeds; Milling machines; Pattern recognition; Signal analysis; Signal processing algorithms; ART-2; Mill condition recognition; cement mill; fault recognition; wavelet;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
Type :
conf
DOI :
10.1109/IITA.2008.329
Filename :
4740027
Link To Document :
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