Title :
e-prognosis and diagnosis for process management using data mining and artificial intelligence
Author :
Bae, Hyeon ; Kim, Sungshin ; Kim, Yeajin ; Lee, Man Hyupg ; Woo, Kwang Bang
Author_Institution :
Sch. of Electr. Eng., Pusan Nat. Univ., South Korea
Abstract :
In the past several decades, the huge amount of data was collected and processed by manufacturers to improve the quality and the productivity of products. Data collection mechanism as one of the process management system is an essential part in the manufacturing processes. Many researchers now devote substantial portions of their day to worrying about data handling that includes extracting information. But, the accumulated records in the real manufacturing processes are not effectively utilized to change operational conditions or remain unused condition. Therefore, the primary goal of this paper is to survey the existing KM techniques and apply the methods to two examples for e-prognosis and e-diagnosis purposes.
Keywords :
artificial intelligence; data mining; knowledge management; manufacturing processes; production engineering computing; production management; artificial intelligence; data collection mechanism; data handling; data mining; e-diagnosis; e-prognosis; information extraction; knowledge management; manufacturing processes; process management; Artificial intelligence; Data mining; Decision making; Delta modulation; Furnaces; Knowledge management; Manufacturing processes; Modems; Virtual manufacturing; Visual databases;
Conference_Titel :
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
Print_ISBN :
0-7803-7906-3
DOI :
10.1109/IECON.2003.1280645