Fuzzy approach to the condition monitoring of a packaging plant

Journal of Materials Processing Technology

Volumn 109, Issue 1-2, 2001, Pages 83-89

  Jeffries, M ^a   Lai, E ^a   Plantenberg, D H ^a   Hull, J B ^a  

^a NOTTINGHAM TRENT UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER AIDED MANUFACTURING; FUZZY SETS; INDUSTRIAL ECONOMICS; PROCESS ENGINEERING;

FUZZY INFERENCE METHOD;

PACKAGING;

EID: 0035254274     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0924-0136(00)00779-2     Document Type: Article

References (7)

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3. Chow M., Magnum P.M., Yee S.O. A neural network approach to real-time condition monitoring of induction motors. IEEE Trans. Ind. Electron. 38(6):1991;448-453.
4. I.E. Aluindigue, et al., Monitoring and diagnosis of rolling element bearings using artificial neural networks, IEEE Trans. Ind. Electron. 40 (2) (1993) 209-216.
5. E. Lai, D.H. Plantenberg, Z.R. Grant, J.B. Hull, Condition monitoring of plant and equipment with the aid of neural networks, in: Proceedings of the Symposium on Mechanics in Design, University Of Toronto, Canada, 6-9 May 1996, pp. 515-522.
6. Li S., Elbestawi M.A. Tool condition monitoring in machining by fuzzy neural networks. J. Dynamic Syst. Measur. Control Trans. ASME. 118:1996;665-672.
7. Das S., Bandyopadhyay P.P., Chattopadhyay A.B. Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool. J. Mater. Proces. Technol. 63(1/3):1997;187-192.