A Machine Learning Approach to Generate Rules for Process Fault Diagnosis

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概要

• 論文の詳細を見る

• 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.

• 社団法人 化学工学会の論文
• 2004-06-01

著者

• Shastri Srinivas

  School Of Engineering Murdoch University

• LAM Chiou-Peng

  School of Computer and Information Science, Edith Cowan University

• WERNER Brenda

  School of Engineering, Murdoch University

• Werner Brenda

  School Of Engineering Murdoch University

• Lam Chiou-peng

  School Of Computer And Information Science Edith Cowan University

関連論文

• A Machine Learning Approach to Generate Rules for Process Fault Diagnosis

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