Please use this identifier to cite or link to this item:
Title: Web-based fault diagnostic and learning system
Authors: Ong, S.K. 
An, N.
Nee, A.Y.C. 
Keywords: Expert systems
Fault diagnosis
Knowledge acquisition
Multi-agent sysems
Issue Date: 2001
Citation: Ong, S.K.,An, N.,Nee, A.Y.C. (2001). Web-based fault diagnostic and learning system. International Journal of Advanced Manufacturing Technology 18 (7) : 502-511. ScholarBank@NUS Repository.
Abstract: Web-based technology holds great potential for enabling the rapid dissemination of information and facilitating distributed decision-making. This paper presents a novel knowledge-based multi-agent system for remote fault diagnosis, which is composed of diagnostic and learning agents (DLAs), machine agents (MAs) and a central management agent (CMA). Machines are remotely diagnosed by the DLAs through the communication channels between the MAs and the DLAs. In addition, the DLAs can learn new expertise front the users, and the CMA can update the central knowledge base (CKB) shared by all the DIAs with the valuable expertise. When faults that cannot be solved with the present knowledge base occur, the DLA can acquire new knowledge, translate it into rules using a rule builder, and update the rules into the CKB. The CKB will become mature through a continuous learning process. A prototype system has been developed and used for remote fault diagnostics of tool wear in computer numerically controlled (CNC) machining.
Source Title: International Journal of Advanced Manufacturing Technology
ISSN: 02683768
DOI: 10.1007/s001700170043
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Oct 6, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.