Automated extraction of diagnostic knowledge

Automated extraction of diagnostic knowledge

With the advent in technology, manufacturing systems have become more complex. It may not be easy for an engineer to acquire sufficient knowledge in a short time in order to carry out manufacturing diagnosis efficiently. As a result, the ability to automatically extract diagnostic rules from the raw...

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Bibliographic Details
Main Author: Zhai, Lianyin
Other Authors: Khoo, Li Pheng
Format: Theses and Dissertations
Language:English
Published: 2008
Subjects:
DRNTU::Engineering::Manufacturing::Production management
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:http://hdl.handle.net/10356/13476
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Institution: Nanyang Technological University
Language: English
Description
Summary:With the advent in technology, manufacturing systems have become more complex. It may not be easy for an engineer to acquire sufficient knowledge in a short time in order to carry out manufacturing diagnosis efficiently. As a result, the ability to automatically extract diagnostic rules from the raw knowledge or data gleaned from a manufacturing system has become an important area in artificial intelligence research. In reality, the raw knowledge gleaned from a manufacturing system may contain uncertainty, that is, it may be imprecise or incomplete.

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