Journal article
A machine learning approach to generate rules for process fault diagnosis
Journal of Chemical Engineering of Japan, Vol.37(6), pp.691-697
2004
Abstract
Expert systems can play a very important role in manufacturing processes by locating problems as soon as they arise. The most important ingredient in any expert system is knowledge. The current knowledge acquisition method is slow and tedious and there exist substantial difficulties in acquiring the knowledge for complex processes. An approach is proposed that makes use of the machine learning technique, C4.5, to generate a decision tree. The decision tree is translated into rules that are implemented into the expert system shell, G2. The rules are tested using a sensitivity analysis of the system. The approach works well, but depends on both the quality and quantity of available training data.
Details
- Title
- A machine learning approach to generate rules for process fault diagnosis
- Authors/Creators
- S. Shastri (Author/Creator) - Murdoch UniversityC.P. Lam (Author/Creator) - Edith Cowan UniversityB. Werner (Author/Creator) - Murdoch University
- Publication Details
- Journal of Chemical Engineering of Japan, Vol.37(6), pp.691-697
- Publisher
- The Society of Chemical Engineers, Japan
- Identifiers
- 991005540951707891
- Copyright
- © 2004 The Society of Chemical Engineers, Japan
- Murdoch Affiliation
- School of Engineering
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.2106 Case-Based Reasoning
- Web Of Science research areas
- Engineering, Chemical
- ESI research areas
- Chemistry