Chemical plant fault diagnosis through a hybrid symbolic-connectionist machine learning approach

A novel hybrid symbolic-connectionist approach to machine learning is introduced and applied to fault diagnosis of a hydrocarbon chlorination plant. The learning algorithm addresses the knowledge acquisition problem by developing and maintaining the knowledge base through instance based inductive learning. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification accuracy on the test cases. Results indicate that the introduced system is a promising alternative to neural networks for fault diagnosis and a complement to expert systems.

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